diff --git a/.env.example b/.env.example
deleted file mode 100644
index 53e921e..0000000
--- a/.env.example
+++ /dev/null
@@ -1,5 +0,0 @@
-# Replace the placeholders with your actual API keys
-
-NEWSAPI_KEY="your_actual_newsapi_key_here"
-GOOGLE_API_KEY="your_actual_google_api_key_here"
-
diff --git a/.github/CICD_SETUP.md b/.github/CICD_SETUP.md
deleted file mode 100644
index c88decd..0000000
--- a/.github/CICD_SETUP.md
+++ /dev/null
@@ -1,239 +0,0 @@
-# GitHub Actions CI Setup Guide
-
-## 🚀 Setting Up Continuous Integration for StockSense Agent
-
-This guide walks you through setting up automated testing with GitHub Actions for your StockSense Agent project.
-
-## 📁 Files Created
-
-### 1. Main CI Workflow: `.github/workflows/ci.yml`
-
-**Complete enterprise-grade CI pipeline with:**
-
-- ✅ Unit testing
-- ✅ Integration testing
-- ✅ Code quality checks
-- ✅ Security scanning
-- ✅ Coverage reporting
-- ✅ Multi-job pipeline
-
-### 2. Simple CI Workflow: `.github/workflows/ci-simple.yml`
-
-**Minimal CI pipeline that exactly matches your requirements:**
-
-- ✅ Triggers on push/PR to main
-- ✅ Python 3.10 setup
-- ✅ Dependency installation
-- ✅ Pytest execution
-- ✅ Environment variable support
-
-## 🔧 Setup Instructions
-
-### Step 1: Choose Your Workflow
-
-**Option A: Use Simple Workflow (Recommended for start)**
-
-```bash
-# Keep only the simple workflow
-mv .github/workflows/ci-simple.yml .github/workflows/ci.yml
-rm .github/workflows/ci-simple.yml
-```
-
-**Option B: Use Full-Featured Workflow**
-
-```bash
-# Keep the comprehensive workflow
-rm .github/workflows/ci-simple.yml
-# The main ci.yml is already in place
-```
-
-### Step 2: Configure GitHub Secrets
-
-In your GitHub repository, go to **Settings > Secrets and Variables > Actions**
-
-Add the following secrets:
-
-#### Required Secrets:
-
-- **`GOOGLE_API_KEY`**: Your Google AI API key for sentiment analysis
-- **`NEWSAPI_KEY`**: Your NewsAPI key for fetching news headlines
-
-#### How to Add Secrets:
-
-1. Go to your GitHub repository
-2. Click **Settings** tab
-3. Navigate to **Secrets and variables** > **Actions**
-4. Click **New repository secret**
-5. Add each secret with the exact names above
-
-### Step 3: Push to GitHub
-
-```bash
-# Add the workflow files
-git add .github/
-
-# Commit the changes
-git commit -m "Add GitHub Actions CI workflow for automated testing"
-
-# Push to main branch (this will trigger the first CI run)
-git push origin main
-```
-
-## 🎯 What Happens After Setup
-
-### Automatic Triggers:
-
-- **Every push to main**: Full CI pipeline runs
-- **Every pull request to main**: CI validation runs
-- **Manual trigger**: Can be run manually from GitHub Actions tab
-
-### Test Execution:
-
-- **Unit Tests**: Tests individual tools (news, price data)
-- **Integration Tests**: Tests API endpoints (requires server)
-- **Smoke Tests**: Quick functionality validation
-
-### CI Pipeline Results:
-
-- ✅ **Green checkmark**: All tests passed
-- ❌ **Red X**: Tests failed (blocks merging)
-- 🟡 **Yellow circle**: Tests running
-
-## 📊 Monitoring CI Results
-
-### View Results:
-
-1. Go to your GitHub repository
-2. Click **Actions** tab
-3. See all workflow runs and their status
-
-### Debug Failed Tests:
-
-1. Click on the failed workflow run
-2. Click on the **Build and Test** job
-3. Expand the failed step to see error details
-4. Review logs and fix issues locally
-
-## 🔄 Workflow Customization
-
-### Modify Test Commands:
-
-Edit the workflow file to change test execution:
-
-```yaml
-# Run specific test types
-- name: Run tests
- run: |
- python run_tests.py unit # Only unit tests
- # OR
- python run_tests.py smoke # Only smoke tests
- # OR
- pytest tests/test_tools.py -v # Specific test file
-```
-
-### Add More Python Versions:
-
-```yaml
-strategy:
- matrix:
- python-version: ['3.9', '3.10', '3.11']
-```
-
-### Add Different Operating Systems:
-
-```yaml
-strategy:
- matrix:
- os: [ubuntu-latest, windows-latest, macos-latest]
-runs-on: ${{ matrix.os }}
-```
-
-## ⚠️ Troubleshooting
-
-### Common Issues:
-
-#### 1. **Tests fail due to missing secrets**
-
-- Ensure `GOOGLE_API_KEY` and `NEWSAPI_KEY` are set in repository secrets
-- Check secret names match exactly (case-sensitive)
-
-#### 2. **Dependencies installation fails**
-
-- Verify `requirements.txt` includes all needed packages
-- Check for conflicts between package versions
-
-#### 3. **Tests timeout**
-
-- Network-dependent tests may be slow
-- Consider mocking external API calls for CI
-
-#### 4. **Import errors**
-
-- Ensure project structure is correct
-- Check that `__init__.py` files exist in packages
-
-### Debug Commands:
-
-```bash
-# Test locally before pushing
-python run_tests.py smoke
-python run_tests.py unit
-
-# Check requirements file
-pip install -r requirements.txt
-
-# Validate workflow syntax
-# Use GitHub CLI or online YAML validators
-```
-
-## 🚀 Next Steps
-
-### 1. **Add Branch Protection Rules**
-
-- Require CI to pass before merging
-- Require pull request reviews
-
-### 2. **Add Code Coverage**
-
-- Use codecov.io integration
-- Set coverage thresholds
-
-### 3. **Add Deployment Pipeline**
-
-- Deploy on successful CI
-- Environment-specific deployments
-
-### 4. **Add Notifications**
-
-- Slack/Discord integration
-- Email notifications on failures
-
-## 📝 Best Practices
-
-### 1. **Keep Secrets Secure**
-
-- Never commit API keys to code
-- Use environment variables consistently
-- Rotate secrets regularly
-
-### 2. **Fast Feedback**
-
-- Run quick tests first
-- Fail fast on syntax errors
-- Parallel job execution
-
-### 3. **Reliable Tests**
-
-- Mock external dependencies when possible
-- Use deterministic test data
-- Handle network timeouts gracefully
-
-### 4. **Clear Documentation**
-
-- Comment workflow steps
-- Document secret requirements
-- Maintain setup instructions
-
----
-
-Your StockSense Agent now has **professional-grade Continuous Integration** that will automatically validate every code change and ensure quality standards! 🎉
diff --git a/.github/CI_STATUS.md b/.github/CI_STATUS.md
deleted file mode 100644
index 179295b..0000000
--- a/.github/CI_STATUS.md
+++ /dev/null
@@ -1,52 +0,0 @@
-# 🔄 CI/CD Status
-
-## Build Status
-
-[](https://github.com/Spkap/StockSense-AI/actions/workflows/ci.yml)
-
-## Quick Setup Commands
-
-### 1. **Choose Your Workflow**
-
-```bash
-# Use the simple workflow (recommended)
-cp .github/workflows/ci-simple.yml .github/workflows/ci.yml
-rm .github/workflows/ci-simple.yml
-
-# OR use the comprehensive workflow (advanced)
-rm .github/workflows/ci-simple.yml
-```
-
-### 2. **Configure Secrets in GitHub**
-
-Go to: **Repository Settings > Secrets and Variables > Actions**
-
-Add these secrets:
-
-- `GOOGLE_API_KEY` - Your Google AI API key
-- `NEWSAPI_KEY` - Your News API key
-
-### 3. **Deploy CI**
-
-```bash
-git add .github/
-git commit -m "Add GitHub Actions CI pipeline"
-git push origin main
-```
-
-## 🎯 What This Achieves
-
-✅ **Automated Testing** - Every push and PR runs full test suite
-✅ **Quality Assurance** - Code quality gates prevent broken code
-✅ **Fast Feedback** - Know immediately if changes break anything
-✅ **Professional Standards** - Industry-standard CI/CD practices
-
-## 📊 Monitoring
-
-- **View CI Status**: Go to repository **Actions** tab
-- **Debug Failures**: Click on failed runs for detailed logs
-- **Badge Status**: Shows current build status in README
-
----
-
-_Your StockSense Agent is now enterprise-ready with automated CI/CD! 🚀_
diff --git a/.github/workflows/ai-service.yml b/.github/workflows/ai-service.yml
new file mode 100644
index 0000000..5d66688
--- /dev/null
+++ b/.github/workflows/ai-service.yml
@@ -0,0 +1,38 @@
+name: AI Service CI/CD
+
+on:
+ push:
+ branches: [ main ]
+ paths:
+ - 'stocksense/**'
+
+jobs:
+ deploy:
+ runs-on: ubuntu-latest
+ if: github.ref == 'refs/heads/main'
+ steps:
+ - name: Checkout repository
+ uses: actions/checkout@v4
+
+ - name: Checkout code
+ uses: actions/checkout@v4
+
+ - name: Install SSH Client
+ run: sudo apt-get install -y openssh-client
+
+ - name: Setup SSH Key
+ run: |
+ mkdir -p ~/.ssh
+ echo "${{ secrets.EC2_KEY }}" > ~/.ssh/id_rsa
+ chmod 600 ~/.ssh/id_rsa
+
+ - name: Deploy to EC2
+ run: |
+ ssh -o StrictHostKeyChecking=no ${{ secrets.EC2_USER }}@${{ secrets.EC2_HOST }} "\
+ export AWS_REGION=${{ secrets.AWS_REGION }} && \
+ export ECR_REPO=${{ secrets.ECR_REPO }} && \
+ aws ecr get-login-password --region \$AWS_REGION | docker login --username AWS --password-stdin \$ECR_REPO && \
+ docker pull \$ECR_REPO:latest && \
+ docker stop stocksense-ai || true && docker rm stocksense-ai || true && \
+ docker run -d -p 8001:8080 --name stocksense-ai \$ECR_REPO:latest \
+ "
diff --git a/.github/workflows/backend.yml b/.github/workflows/backend.yml
new file mode 100644
index 0000000..734f76a
--- /dev/null
+++ b/.github/workflows/backend.yml
@@ -0,0 +1,174 @@
+name: Backend CI/CD
+
+on:
+ push:
+ branches: [ main ]
+ paths:
+ - 'backend/**'
+ pull_request:
+ branches: [ main ]
+ paths:
+ - 'backend/**'
+
+jobs:
+ test:
+ runs-on: ubuntu-latest
+
+ services:
+ postgres:
+ image: postgres:15
+ env:
+ POSTGRES_USER: postgres
+ POSTGRES_PASSWORD: postgres
+ POSTGRES_DB: test_db
+ ports:
+ - 5432:5432
+ options: >-
+ --health-cmd="pg_isready -U postgres"
+ --health-interval=10s
+ --health-timeout=5s
+ --health-retries=5
+
+ env:
+ DJANGO_SETTINGS_MODULE: backend.settings
+ DATABASE_URL: postgres://postgres:postgres@localhost:5432/test_db
+ SECRET_KEY: test-secret
+ DEBUG: "0"
+ TESTING: "true"
+
+ steps:
+ - name: Checkout code
+ uses: actions/checkout@v4
+
+ - name: Set up Python
+ uses: actions/setup-python@v5
+ with:
+ python-version: "3.11"
+
+ - name: Install dependencies
+ working-directory: backend
+ run: |
+ python -m pip install --upgrade pip
+ pip install -r requirements.txt
+
+ - name: Wait for Postgres
+ run: |
+ until pg_isready -h localhost -p 5432 -U postgres; do
+ echo "Waiting for Postgres..."
+ sleep 2
+ done
+
+ - name: Run migrations
+ working-directory: backend
+ run: python manage.py migrate
+
+ - name: Run tests
+ working-directory: backend
+ run: python manage.py test --noinput
+
+ deploy:
+ runs-on: ubuntu-latest
+ needs: test
+ if: github.ref == 'refs/heads/main'
+ env:
+ AWS_REGION: ${{ secrets.AWS_REGION }}
+
+
+ steps:
+ - name: Checkout code
+ uses: actions/checkout@v4
+
+ - name: Set up Docker Buildx
+ uses: docker/setup-buildx-action@v3
+
+ - name: Configure AWS credentials
+ uses: aws-actions/configure-aws-credentials@v4
+ with:
+ aws-access-key-id: ${{ secrets.AWS_ACCESS_KEY_ID }}
+ aws-secret-access-key: ${{ secrets.AWS_SECRET_ACCESS_KEY }}
+ aws-region: ${{ secrets.AWS_REGION }}
+
+
+
+ - name: Login to Amazon ECR
+ uses: aws-actions/amazon-ecr-login@v2
+ with:
+ registry-type: private
+
+ - name: Create .env file
+ run: |
+ echo "DATABASE_URL=${{ secrets.DATABASE_URL }}" >> backend/.env
+ echo "SECRET_KEY=${{ secrets.SECRET_KEY }}" >> backend/.env
+ echo "DEBUG=${{ secrets.DEBUG }}" >> backend/.env
+ echo "FIREBASE_TYPE=${{ secrets.FIREBASE_TYPE }}" >> backend/.env
+ echo "FIREBASE_PROJECT_ID=${{ secrets.FIREBASE_PROJECT_ID }}" >> backend/.env
+ echo "FIREBASE_PRIVATE_KEY_ID=${{ secrets.FIREBASE_PRIVATE_KEY_ID }}" >> backend/.env
+ echo "FIREBASE_PRIVATE_KEY=${{ secrets.FIREBASE_PRIVATE_KEY }}" >> backend/.env
+ echo "FIREBASE_CLIENT_EMAIL=${{ secrets.FIREBASE_CLIENT_EMAIL }}" >> backend/.env
+ echo "FIREBASE_CLIENT_ID=${{ secrets.FIREBASE_CLIENT_ID }}" >> backend/.env
+ echo "FIREBASE_AUTH_URI=${{ secrets.FIREBASE_AUTH_URI }}" >> backend/.env
+ echo "FIREBASE_TOKEN_URI=${{ secrets.FIREBASE_TOKEN_URI }}" >> backend/.env
+ echo "FIREBASE_AUTH_PROVIDER_X509_CERT_URL=${{ secrets.FIREBASE_AUTH_PROVIDER_X509_CERT_URL }}" >> backend/.env
+ echo "FIREBASE_CLIENT_X509_CERT_URL=${{ secrets.FIREBASE_CLIENT_X509_CERT_URL }}" >> backend/.env
+ echo "FIREBASE_UNIVERSE_DOMAIN=${{ secrets.FIREBASE_UNIVERSE_DOMAIN }}" >> backend/.env
+ echo "FIREBASE_STORAGE_BUCKET=${{ secrets.FIREBASE_STORAGE_BUCKET }}" >> backend/.env
+
+ - name: Build and push Docker image
+ uses: docker/build-push-action@v5
+ with:
+ context: ./backend
+ push: true
+ tags: ${{ secrets.ECR_REPO }}:latest
+
+ - name: Install SSH Client
+ run: sudo apt-get install -y openssh-client
+
+ - name: Setup SSH Key
+ run: |
+ mkdir -p ~/.ssh
+ echo "${{ secrets.EC2_KEY }}" > ~/.ssh/id_rsa
+ chmod 600 ~/.ssh/id_rsa
+
+ - name: Deploy to EC2
+ run: |
+ ssh -o StrictHostKeyChecking=no ${{ secrets.EC2_USER }}@${{ secrets.EC2_HOST }} "\
+ docker pull ${{ secrets.ECR_REPO }}:latest && \
+ docker stop stocksense || true && \
+ docker rm stocksense || true && \
+ docker run -d -p 8000:8000 --name stocksense \
+ -e DATABASE_URL='${{ secrets.DATABASE_URL }}' \
+ -e SECRET_KEY='${{ secrets.SECRET_KEY }}' \
+ -e DEBUG='${{ secrets.DEBUG }}' \
+ -e FIREBASE_TYPE='${{ secrets.FIREBASE_TYPE }}' \
+ -e FIREBASE_PROJECT_ID='${{ secrets.FIREBASE_PROJECT_ID }}' \
+ -e FIREBASE_PRIVATE_KEY_ID='${{ secrets.FIREBASE_PRIVATE_KEY_ID }}' \
+ -e FIREBASE_PRIVATE_KEY='${{ secrets.FIREBASE_PRIVATE_KEY }}' \
+ -e FIREBASE_CLIENT_EMAIL='${{ secrets.FIREBASE_CLIENT_EMAIL }}' \
+ -e FIREBASE_CLIENT_ID='${{ secrets.FIREBASE_CLIENT_ID }}' \
+ -e FIREBASE_AUTH_URI='${{ secrets.FIREBASE_AUTH_URI }}' \
+ -e FIREBASE_TOKEN_URI='${{ secrets.FIREBASE_TOKEN_URI }}' \
+ -e FIREBASE_AUTH_PROVIDER_X509_CERT_URL='${{ secrets.FIREBASE_AUTH_PROVIDER_X509_CERT_URL }}' \
+ -e FIREBASE_CLIENT_X509_CERT_URL='${{ secrets.FIREBASE_CLIENT_X509_CERT_URL }}' \
+ -e FIREBASE_UNIVERSE_DOMAIN='${{ secrets.FIREBASE_UNIVERSE_DOMAIN }}' \
+ -e FIREBASE_STORAGE_BUCKET='${{ secrets.FIREBASE_STORAGE_BUCKET }}' \
+ ${{ secrets.ECR_REPO }}:latest"
+
+ - name: Health Check
+ run: |
+ ssh -o StrictHostKeyChecking=no ${{ secrets.EC2_USER }}@${{ secrets.EC2_HOST }} "\
+ echo 'Waiting for container to start...' && \
+ sleep 30 && \
+ echo 'Checking container status...' && \
+ docker ps | grep stocksense && \
+ echo 'Testing health endpoint...' && \
+ RESPONSE=\$(curl -s -w '%{http_code}' http://localhost:8000/ 2>/dev/null); \
+ HTTP_CODE=\${RESPONSE: -3}; \
+ BODY=\${RESPONSE%???}; \
+ if [ \"\$HTTP_CODE\" = \"200\" ]; then \
+ echo 'Health check passed!'; \
+ echo 'Response: '\$BODY; \
+ else \
+ echo 'Health check failed (HTTP \$HTTP_CODE)'; \
+ docker logs stocksense --tail 50; \
+ exit 1; \
+ fi"
diff --git a/.github/workflows/ci-simple.yml b/.github/workflows/ci-simple.yml
deleted file mode 100644
index 1413967..0000000
--- a/.github/workflows/ci-simple.yml
+++ /dev/null
@@ -1,50 +0,0 @@
-# GitHub Actions CI Workflow for StockSense Agent
-# Automated testing pipeline for Python application
-
-name: Python Application CI
-
-# Trigger the workflow on push and pull requests to main branch
-on:
- push:
- branches: [main]
- pull_request:
- branches: [main]
-
-# Define the CI job
-jobs:
- build:
- # Job name and runner configuration
- name: Build and Test
- runs-on: ubuntu-latest
-
- steps:
- # Step 1: Checkout code from repository
- - name: Checkout code
- uses: actions/checkout@v4
-
- # Step 2: Set up Python 3.10 environment
- - name: Set up Python 3.10
- uses: actions/setup-python@v5
- with:
- python-version: '3.10'
-
- # Step 3: Install dependencies
- - name: Upgrade pip
- run: |
- python -m pip install --upgrade pip
-
- - name: Install project dependencies
- run: |
- pip install -r requirements.txt
-
- - name: Install testing dependencies
- run: |
- pip install pytest requests
-
- # Step 4: Run tests with environment variables
- - name: Run tests
- run: |
- pytest tests/ -v
- env:
- GOOGLE_API_KEY: ${{ secrets.GOOGLE_API_KEY }}
- NEWSAPI_KEY: ${{ secrets.NEWSAPI_KEY }}
diff --git a/.github/workflows/ci.yml b/.github/workflows/ci.yml
deleted file mode 100644
index 1cfe621..0000000
--- a/.github/workflows/ci.yml
+++ /dev/null
@@ -1,194 +0,0 @@
-# GitHub Actions CI Workflow for StockSense Agent
-# This workflow runs automated tests on every push and pull request to main branch
-
-name: Python Application CI
-
-# Trigger Configuration
-# Runs on pushes to main branch and all pull requests targeting main
-on:
- push:
- branches: [main]
- pull_request:
- branches: [main]
-
-# Define CI Jobs
-jobs:
- build:
- # Job Configuration
- name: Build and Test
- runs-on: ubuntu-latest
-
- # Define build steps
- steps:
- # Step 1: Checkout Repository Code
- - name: Checkout code
- uses: actions/checkout@v4
- with:
- # Fetch full history for better Git operations
- fetch-depth: 0
-
- # Step 2: Set up Python Environment
- - name: Set up Python 3.10
- uses: actions/setup-python@v5
- with:
- python-version: '3.10'
- # Cache pip dependencies for faster builds
- cache: 'pip'
-
- # Step 3: Install Dependencies
- - name: Upgrade pip
- run: |
- python -m pip install --upgrade pip
-
- - name: Install project dependencies
- run: |
- pip install -r requirements.txt
-
- - name: Install testing dependencies
- run: |
- pip install pytest requests
-
- # Step 4: Run Linting (Optional but recommended)
- - name: Run code quality checks
- run: |
- # Install flake8 for linting
- pip install flake8
- # Stop the build if there are Python syntax errors or undefined names
- flake8 . --count --select=E9,F63,F7,F82 --show-source --statistics
- # Exit-zero treats all errors as warnings. GitHub editor is 127 chars wide
- flake8 . --count --exit-zero --max-complexity=10 --max-line-length=127 --statistics
- continue-on-error: true
-
- # Step 5: Run Unit Tests (No external dependencies required)
- - name: Run unit tests
- run: |
- # Run only unit tests that don't require external services
- python run_tests.py unit
- env:
- # Set environment variables from GitHub secrets
- GOOGLE_API_KEY: ${{ secrets.GOOGLE_API_KEY }}
- NEWSAPI_KEY: ${{ secrets.NEWSAPI_KEY }}
-
- # Step 6: Run Smoke Tests (Quick validation)
- - name: Run smoke tests
- run: |
- # Run quick smoke tests for basic functionality validation
- python run_tests.py smoke
- env:
- GOOGLE_API_KEY: ${{ secrets.GOOGLE_API_KEY }}
- NEWSAPI_KEY: ${{ secrets.NEWSAPI_KEY }}
-
- # Step 7: Run Full Test Suite with Coverage (Optional)
- - name: Run tests with coverage
- run: |
- # Install coverage tool
- pip install coverage
- # Run pytest with coverage reporting
- coverage run -m pytest tests/test_tools.py -v
- # Generate coverage report
- coverage report -m
- # Generate HTML coverage report
- coverage html
- env:
- GOOGLE_API_KEY: ${{ secrets.GOOGLE_API_KEY }}
- NEWSAPI_KEY: ${{ secrets.NEWSAPI_KEY }}
- continue-on-error: true
-
- # Step 8: Upload Coverage Reports (Optional)
- - name: Upload coverage reports
- uses: actions/upload-artifact@v4
- with:
- name: coverage-report
- path: htmlcov/
- if: always()
-
- # Step 9: Upload Test Results
- - name: Upload test results
- uses: actions/upload-artifact@v4
- with:
- name: test-results
- path: |
- .pytest_cache/
- test-results.xml
- if: always()
-
- # Additional Jobs for Different Scenarios
- integration-tests:
- # Only run integration tests if unit tests pass
- needs: build
- name: Integration Tests
- runs-on: ubuntu-latest
-
- steps:
- - name: Checkout code
- uses: actions/checkout@v4
-
- - name: Set up Python 3.10
- uses: actions/setup-python@v5
- with:
- python-version: '3.10'
- cache: 'pip'
-
- - name: Install dependencies
- run: |
- python -m pip install --upgrade pip
- pip install -r requirements.txt
- pip install pytest requests
-
- # Start the API server in background for integration tests
- - name: Start API server
- run: |
- # Start the StockSense API server in background
- python -m stocksense.main &
- # Wait for server to start
- sleep 10
- env:
- GOOGLE_API_KEY: ${{ secrets.GOOGLE_API_KEY }}
- NEWSAPI_KEY: ${{ secrets.NEWSAPI_KEY }}
-
- - name: Run integration tests
- run: |
- # Run API integration tests
- python run_tests.py api
- env:
- GOOGLE_API_KEY: ${{ secrets.GOOGLE_API_KEY }}
- NEWSAPI_KEY: ${{ secrets.NEWSAPI_KEY }}
-
- # Security and Code Quality Job
- security-scan:
- name: Security Scan
- runs-on: ubuntu-latest
-
- steps:
- - name: Checkout code
- uses: actions/checkout@v4
-
- - name: Set up Python 3.10
- uses: actions/setup-python@v5
- with:
- python-version: '3.10'
-
- - name: Install security tools
- run: |
- python -m pip install --upgrade pip
- pip install safety bandit
-
- - name: Run safety check
- run: |
- # Check for known security vulnerabilities in dependencies
- safety check --file requirements.txt
- continue-on-error: true
-
- - name: Run bandit security scan
- run: |
- # Run security analysis on Python code
- bandit -r stocksense/ -f json -o bandit-report.json
- continue-on-error: true
-
- - name: Upload security reports
- uses: actions/upload-artifact@v4
- with:
- name: security-reports
- path: |
- bandit-report.json
- if: always()
diff --git a/.github/workflows/frontend.yml b/.github/workflows/frontend.yml
new file mode 100644
index 0000000..2b50eaf
--- /dev/null
+++ b/.github/workflows/frontend.yml
@@ -0,0 +1,33 @@
+name: Frontend CI/CD
+
+on:
+ push:
+ branches: [ main ]
+ paths:
+ - 'frontend/**'
+
+jobs:
+ build-and-deploy:
+ runs-on: ubuntu-latest
+
+ steps:
+ - name: Checkout code
+ uses: actions/checkout@v4
+
+ - name: Setup Node
+ uses: actions/setup-node@v4
+ with:
+ node-version: 18
+
+ - name: Install dependencies
+ run: npm install
+ working-directory: frontend
+
+ - name: Build app
+ run: npm run build
+ working-directory: frontend
+
+ - name: Trigger Render deployment
+ run: curl -X POST "$RENDER_DEPLOY_HOOK"
+ env:
+ RENDER_DEPLOY_HOOK: ${{ secrets.RENDER_DEPLOY_HOOK }}
diff --git a/.gitignore b/.gitignore
index 16cccec..37f2ac6 100644
--- a/.gitignore
+++ b/.gitignore
@@ -38,6 +38,7 @@ parts/
sdist/
var/
wheels/
+.vscode/
*.egg-info/
.installed.cfg
*.egg
@@ -108,3 +109,7 @@ docker-compose.yml
.Trashes
ehthumbs.db
Thumbs.db
+
+stocksense-e7226-firebase-adminsdk-fbsvc-b8cae3c098.json
+nasdaq_screener_1756622606980.csv
+data.json
\ No newline at end of file
diff --git a/.vscode/settings.json b/.vscode/settings.json
deleted file mode 100644
index a32e2b4..0000000
--- a/.vscode/settings.json
+++ /dev/null
@@ -1,5 +0,0 @@
-{
- "python-envs.defaultEnvManager": "ms-python.python:conda",
- "python-envs.defaultPackageManager": "ms-python.python:conda",
- "python-envs.pythonProjects": []
-}
diff --git a/.vscode/tasks.json b/.vscode/tasks.json
deleted file mode 100644
index 17b85ec..0000000
--- a/.vscode/tasks.json
+++ /dev/null
@@ -1,13 +0,0 @@
-{
- "version": "2.0.0",
- "tasks": [
- {
- "label": "Run StockSense ReAct Agent",
- "type": "shell",
- "command": "cd /Users/sourabhkapure/Developer/Projects/StockSense-Agent && python -m stocksense.main",
- "group": "build",
- "isBackground": true,
- "problemMatcher": []
- }
- ]
-}
diff --git a/Dockerfile.backend b/Dockerfile.backend
deleted file mode 100644
index e627911..0000000
--- a/Dockerfile.backend
+++ /dev/null
@@ -1,34 +0,0 @@
-# Use Python 3.10 slim image for smaller size
-FROM python:3.10-slim
-
-# Set working directory
-WORKDIR /app
-
-# Install system dependencies
-RUN apt-get update && apt-get install -y \
- gcc \
- g++ \
- curl \
- && rm -rf /var/lib/apt/lists/*
-
-# Copy requirements first (for better caching)
-COPY requirements.txt .
-
-# Install Python dependencies
-RUN pip install --no-cache-dir -r requirements.txt
-
-# Copy application code
-COPY . .
-
-# Create directory for SQLite database
-RUN mkdir -p /app/data
-
-# Expose port for FastAPI backend
-EXPOSE 8000
-
-# Health check for backend API
-HEALTHCHECK --interval=30s --timeout=30s --start-period=5s --retries=3 \
- CMD curl -f http://localhost:8000/health || exit 1
-
-# Run the FastAPI backend application
-CMD ["python", "-m", "stocksense.main"]
diff --git a/Dockerfile.frontend b/Dockerfile.frontend
deleted file mode 100644
index b9fdf9d..0000000
--- a/Dockerfile.frontend
+++ /dev/null
@@ -1,41 +0,0 @@
-# Use Python 3.10 slim image
-FROM python:3.10-slim
-
-# Set working directory
-WORKDIR /app
-
-# Install system dependencies
-RUN apt-get update && apt-get install -y \
- gcc \
- g++ \
- curl \
- && rm -rf /var/lib/apt/lists/*
-
-# Copy requirements first (for better caching)
-COPY requirements.txt .
-
-# Install Python dependencies
-RUN pip install --no-cache-dir -r requirements.txt
-
-# Copy application code
-COPY . .
-
-# Expose port for Streamlit
-EXPOSE 8501
-
-# Create Streamlit config directory
-RUN mkdir -p ~/.streamlit
-
-# Configure Streamlit
-RUN echo '[server]' > ~/.streamlit/config.toml && \
- echo 'headless = true' >> ~/.streamlit/config.toml && \
- echo 'port = 8501' >> ~/.streamlit/config.toml && \
- echo 'enableCORS = false' >> ~/.streamlit/config.toml && \
- echo 'enableXsrfProtection = false' >> ~/.streamlit/config.toml
-
-# Health check
-HEALTHCHECK --interval=30s --timeout=30s --start-period=5s --retries=3 \
- CMD curl -f http://localhost:8501/_stcore/health || exit 1
-
-# Run Streamlit app
-CMD ["streamlit", "run", "app.py", "--server.address", "0.0.0.0"]
diff --git a/app.py b/app.py
deleted file mode 100644
index f8b3778..0000000
--- a/app.py
+++ /dev/null
@@ -1,1202 +0,0 @@
-import streamlit as st
-import requests
-import time
-import pandas as pd
-import numpy as np
-from datetime import datetime, timedelta
-from typing import Optional, Dict, Any
-import json
-import yfinance as yf
-import plotly.graph_objects as go
-import plotly.express as px
-from pathlib import Path
-import os
-PLOTLY_AVAILABLE = True
-
-st.set_page_config(
- page_title="StockSense AI Agent",
- page_icon="📈",
- layout="wide",
- initial_sidebar_state="expanded",
- menu_items={
- 'Get Help': 'https://github.com/Spkap/StockSense-AI',
- 'Report a bug': 'https://github.com/Spkap/StockSense-AI/issues',
- 'About': "# StockSense ReAct Agent\nAI-powered autonomous stock analysis using ReAct pattern"
- }
-)
-
-st.markdown("""
-
-""", unsafe_allow_html=True)
-
-def initialize_session_state():
- if 'analysis_result' not in st.session_state:
- st.session_state.analysis_result = None
- if 'analysis_history' not in st.session_state:
- st.session_state.analysis_history = []
- if 'backend_status' not in st.session_state:
- st.session_state.backend_status = None
- if 'selected_ticker' not in st.session_state:
- st.session_state.selected_ticker = ""
-
-initialize_session_state()
-
-@st.cache_data
-def load_company_ticker_mapping():
- """Load company name to ticker mapping from CSV file."""
- csv_path = Path("nasdaq_screener.csv")
-
- if csv_path.exists():
- try:
- df = pd.read_csv(csv_path)
- if 'Symbol' in df.columns and 'Name' in df.columns:
- # Create a mapping from company name to ticker
- mapping = dict(zip(df['Name'], df['Symbol']))
- return mapping, df
- except Exception as e:
- st.warning(f"Error loading CSV file: {e}")
-
- # Fallback to popular stocks if CSV not available
- fallback_mapping = {
- "Apple Inc.": "AAPL",
- "Microsoft Corporation": "MSFT",
- "Alphabet Inc.": "GOOGL",
- "Amazon.com Inc.": "AMZN",
- "Tesla Inc.": "TSLA",
- "NVIDIA Corporation": "NVDA",
- "Meta Platforms Inc.": "META",
- "Netflix Inc.": "NFLX",
- "Advanced Micro Devices Inc.": "AMD",
- "Intel Corporation": "INTC"
- }
-
- return fallback_mapping, None
-
-def create_candlestick_chart(price_data: list) -> go.Figure:
- """Create an interactive candlestick chart with moving averages."""
- if not price_data:
- return None
-
- # Convert to DataFrame
- df = pd.DataFrame(price_data)
- df['Date'] = pd.to_datetime(df['Date'])
- df = df.sort_values('Date')
-
- # Calculate moving averages
- df['SMA_20'] = df['Close'].rolling(window=20, min_periods=1).mean()
- df['SMA_50'] = df['Close'].rolling(window=50, min_periods=1).mean()
-
- # Create candlestick chart
- fig = go.Figure()
-
- # Add candlestick
- fig.add_trace(go.Candlestick(
- x=df['Date'],
- open=df['Open'],
- high=df['High'],
- low=df['Low'],
- close=df['Close'],
- name="Price",
- increasing_line_color='#26a69a',
- decreasing_line_color='#ef5350'
- ))
-
- # Add moving averages
- fig.add_trace(go.Scatter(
- x=df['Date'],
- y=df['SMA_20'],
- mode='lines',
- name='SMA 20',
- line=dict(color='orange', width=2)
- ))
-
- fig.add_trace(go.Scatter(
- x=df['Date'],
- y=df['SMA_50'],
- mode='lines',
- name='SMA 50',
- line=dict(color='blue', width=2)
- ))
-
- # Update layout
- fig.update_layout(
- title="Stock Price with Moving Averages",
- yaxis_title="Price ($)",
- xaxis_title="Date",
- template="plotly_white",
- height=500,
- showlegend=True,
- xaxis_rangeslider_visible=False
- )
-
- return fig
-
-BACKEND_URL = os.getenv("BACKEND_URL", "http://127.0.0.1:8000")
-
-def check_backend_status() -> bool:
- """Check if the backend is online."""
- try:
- response = requests.get(f"{BACKEND_URL}/health", timeout=5)
- status = response.status_code == 200
- st.session_state.backend_status = status
- return status
- except:
- st.session_state.backend_status = False
- return False
-
-
-def create_styled_header():
- st.markdown("""
-
-
- 📈 StockSense AI Agent
-
-
- AI-Powered Stock Analysis Using Reasoning & Action
-
-
- """, unsafe_allow_html=True)
-
-
-def display_hero_section():
- create_styled_header()
-
- col1, col2, col3 = st.columns([1, 2, 1])
- with col2:
- if check_backend_status():
- st.success("🟢 Backend Connected & Ready", icon="✅")
- else:
- st.error("🔴 Backend Connection Failed", icon="❌")
-
- st.markdown(" ", unsafe_allow_html=True)
-
-
-def display_ticker_input():
- st.markdown("### Select Stock to Analyze")
-
- # Load company mapping
- company_mapping, df = load_company_ticker_mapping()
- company_names = list(company_mapping.keys())
-
- col1, col2 = st.columns([2, 1])
-
- with col1:
- st.markdown("**Select Company:**")
-
- # Company selectbox
- selected_company = st.selectbox(
- "Choose a company",
- options=[""] + company_names,
- index=0,
- help="Select a company from the dropdown",
- label_visibility="collapsed"
- )
-
- if selected_company:
- ticker = company_mapping[selected_company]
- st.session_state.selected_ticker = ticker
- st.info(f"Selected: **{selected_company}** → **{ticker}**")
-
- with col2:
- st.markdown("**Or enter manually:**")
- manual_ticker = st.text_input(
- "Stock Ticker",
- value=st.session_state.selected_ticker if not selected_company else "",
- placeholder="e.g., AAPL",
- help="Enter any valid stock ticker symbol",
- label_visibility="collapsed"
- )
-
- if manual_ticker and manual_ticker != st.session_state.selected_ticker:
- st.session_state.selected_ticker = manual_ticker.upper().strip()
-
- # Quick select buttons for popular stocks
- st.markdown("**Quick Select Popular Stocks:**")
- cols = st.columns(5)
- popular_tickers = ["AAPL", "MSFT", "GOOGL", "AMZN", "TSLA"]
-
- for i, ticker in enumerate(popular_tickers):
- with cols[i]:
- if st.button(ticker, key=f"quick_{ticker}", use_container_width=True):
- st.session_state.selected_ticker = ticker
-
- cols = st.columns(5)
- more_tickers = ["NVDA", "META", "NFLX", "AMD", "INTC"]
-
- for i, ticker in enumerate(more_tickers):
- with cols[i]:
- if st.button(ticker, key=f"quick_{ticker}", use_container_width=True):
- st.session_state.selected_ticker = ticker
-
- return st.session_state.selected_ticker
-
-
-def validate_ticker(ticker: str) -> tuple[bool, str]:
- """Validate ticker input."""
- if not ticker:
- return False, "Please select or enter a stock ticker symbol"
-
- if not ticker.replace('.', '').isalpha() or len(ticker) < 1 or len(ticker) > 10:
- return False, "Please enter a valid ticker (1-10 letters, dots allowed)"
-
- return True, ""
-
-
-def trigger_analysis(ticker: str) -> Optional[Dict[str, Any]]:
- """Triggers stock analysis via backend API."""
- try:
- if not check_backend_status():
- st.error("🔌 Backend server is offline. Please start the FastAPI server.")
- return None
-
- with st.spinner("🤖 ReAct Agent is analyzing..."):
- response = requests.post(
- f"{BACKEND_URL}/analyze/{ticker}",
- timeout=60
- )
-
- if response.status_code == 200:
- result = response.json()
-
- st.success(f"Analysis for **{ticker}** has been triggered!")
-
- # Check if this is a fresh analysis or cached result
- analysis_data = result.get('data', {})
- if analysis_data.get('source') == 'react_analysis':
- # Fresh analysis - use the data directly from /analyze response
- st.success("✅ Fresh analysis completed!")
-
- result_obj = {
- 'ticker': ticker,
- 'data': analysis_data,
- 'timestamp': datetime.now().isoformat(),
- 'success': True
- }
-
- st.session_state.analysis_result = result_obj
- st.session_state.analysis_history.insert(0, result_obj)
- if len(st.session_state.analysis_history) > 10:
- st.session_state.analysis_history.pop()
-
- return result_obj
-
- elif analysis_data.get('source') == 'cache':
- # Cached result - data is already complete
- st.info("📚 Retrieved from cache")
-
- result_obj = {
- 'ticker': ticker,
- 'data': analysis_data,
- 'timestamp': datetime.now().isoformat(),
- 'success': True
- }
-
- st.session_state.analysis_result = result_obj
- st.session_state.analysis_history.insert(0, result_obj)
- if len(st.session_state.analysis_history) > 10:
- st.session_state.analysis_history.pop()
-
- return result_obj
-
- else:
- # Fallback to old behavior for backward compatibility
- with st.expander("Backend Response Details"):
- st.json(result)
-
- st.markdown("---")
- progress_bar = st.progress(0)
- status_text = st.empty()
-
- max_attempts = 10
- for attempt in range(1, max_attempts + 1):
- status_text.text(f"Fetching results... (Attempt {attempt}/{max_attempts})")
- progress_bar.progress(attempt / max_attempts)
-
- try:
- results_response = requests.get(
- f"{BACKEND_URL}/results/{ticker}",
- timeout=10
- )
-
- if results_response.status_code == 200:
- analysis_data = results_response.json()
- status_text.text("Results retrieved successfully!")
- progress_bar.progress(1.0)
-
- result_obj = {
- 'ticker': ticker,
- 'data': analysis_data.get('data', analysis_data),
- 'timestamp': datetime.now().isoformat(),
- 'success': True
- }
-
- st.session_state.analysis_result = result_obj
-
- st.session_state.analysis_history.insert(0, result_obj)
- if len(st.session_state.analysis_history) > 10:
- st.session_state.analysis_history.pop()
-
- progress_bar.empty()
- status_text.empty()
- return result_obj
-
- elif results_response.status_code == 404:
- if attempt < max_attempts:
- time.sleep(2)
- continue
- else:
- st.error(f"❌ Error fetching results: Status {results_response.status_code}")
- break
-
- except requests.exceptions.RequestException as e:
- st.error(f"❌ Network error: {str(e)}")
- break
-
- progress_bar.empty()
- status_text.empty()
- st.error("⏱️ Analysis timed out. Please try again.")
- return None
-
- else:
- st.error(f"❌ Analysis failed: Status {response.status_code}")
- if response.text:
- st.error(f"Details: {response.text}")
- return None
-
- except requests.exceptions.Timeout:
- st.error("⏱️ Request timed out. Analysis may still be processing.")
- return None
- except requests.exceptions.ConnectionError:
- st.error("🔌 Cannot connect to backend. Please ensure the server is running.")
- return None
- except Exception as e:
- st.error(f"❌ Unexpected error: {str(e)}")
- return None
-
-
-def display_enhanced_analysis_results(data: Dict[str, Any], ticker: str):
- """Display enhanced analysis results with tabbed interface."""
-
- # Create tabs
- tab1, tab2, tab3 = st.tabs(["📊 Analysis Dashboard", "📰 News & Sentiment", "⚙️ Agent Reasoning"])
-
- with tab1:
- st.markdown("### 📊 Analysis Dashboard")
-
- # Display analysis summary
- summary = (data.get('summary') or
- data.get('analysis_summary') or
- data.get('analysis') or
- "Analysis completed successfully")
-
- if summary and summary != "Analysis completed successfully":
- st.markdown(f"""
-
-
- 📊 Analysis Summary
-
-
Stock: {ticker}
-
{summary}
-
- """, unsafe_allow_html=True)
- else:
- st.info("📊 Detailed analysis summary not available for this request")
-
- # Display interactive candlestick chart
- price_data = data.get('price_data', [])
- if not price_data:
- # Try alternative key names
- price_data = (data.get('historical_data') or
- data.get('price_history') or
- data.get('ohlcv_data') or [])
-
- if price_data and len(price_data) > 0:
- st.markdown("#### 📈 Interactive Price Chart with Moving Averages")
-
- try:
- fig = create_candlestick_chart(price_data)
- if fig:
- st.plotly_chart(fig, use_container_width=True)
- st.success(f"📊 Displaying {len(price_data)} days of price data with technical indicators")
- else:
- st.warning("Unable to create candlestick chart - trying fallback visualization")
- # Fallback to simple line chart
- try:
- df = pd.DataFrame(price_data)
- df['Date'] = pd.to_datetime(df['Date'])
- df = df.sort_values('Date').set_index('Date')
- st.line_chart(df[['Close']], use_container_width=True)
- except Exception as fallback_error:
- st.error(f"Chart creation failed: {str(fallback_error)}")
- except Exception as e:
- st.error(f"Error creating chart: {str(e)}")
- # Show raw data for debugging
- with st.expander("🔍 Debug: Raw Price Data"):
- st.json(price_data[:3]) # Show first 3 records
- else:
- # Show more helpful message
- available_keys = list(data.keys())
- st.info(f"📈 No historical price data available in this analysis. Available data: {', '.join(available_keys)}")
-
- # Try to get real-time price data as fallback
- if st.button("🔄 Fetch Current Price Data", key="fetch_price_fallback"):
- with st.spinner("Fetching current market data..."):
- try:
- from stocksense.data_collectors import get_price_history
- df = get_price_history(ticker, period="1mo")
- if df is not None and not df.empty:
- # Convert to our format
- df_reset = df.reset_index()
- df_reset['Date'] = df_reset['Date'].dt.strftime('%Y-%m-%d')
- fallback_data = df_reset.to_dict(orient='records')
-
- fig = create_candlestick_chart(fallback_data)
- if fig:
- st.plotly_chart(fig, use_container_width=True)
- st.success("📊 Showing current market data (not from AI analysis)")
- except Exception as e:
- st.error(f"Failed to fetch fallback data: {str(e)}")
-
- # Display key metrics
- display_key_metrics(ticker)
-
- with tab2:
- st.markdown("### 📰 News & Sentiment Analysis")
-
- # Display detailed sentiment report
- sentiment_report_raw = data.get('sentiment_report')
-
- if sentiment_report_raw:
- st.markdown("""
-
- 📊 Market Sentiment Report
-
- """, unsafe_allow_html=True)
-
- # Try to parse as JSON first
- if isinstance(sentiment_report_raw, str):
- try:
- sentiment_data = json.loads(sentiment_report_raw)
- if isinstance(sentiment_data, list):
- # Display structured sentiment data
- for i, item in enumerate(sentiment_data, 1):
- headline = item.get('headline', 'N/A')
- sentiment = item.get('sentiment', 'N/A')
- justification = item.get('justification', 'N/A')
-
- with st.expander(f"📰 Article {i}: {headline[:80]}..."):
- st.markdown(f"**Headline:** {headline}")
- st.markdown(f"**Sentiment:** {sentiment}")
- st.markdown(f"**Analysis:** {justification}")
- else:
- st.markdown(sentiment_report_raw)
- except json.JSONDecodeError:
- st.markdown(sentiment_report_raw)
- elif isinstance(sentiment_report_raw, list):
- # Already structured data
- for i, item in enumerate(sentiment_report_raw, 1):
- headline = item.get('headline', 'N/A')
- sentiment = item.get('sentiment', 'N/A')
- justification = item.get('justification', 'N/A')
-
- with st.expander(f"� Article {i}: {headline[:80]}..."):
- st.markdown(f"**Headline:** {headline}")
- st.markdown(f"**Sentiment:** {sentiment}")
- st.markdown(f"**Analysis:** {justification}")
- else:
- st.markdown(str(sentiment_report_raw))
- else:
- # Try to show any related sentiment data
- sentiment_info = []
- if data.get('headlines_count'):
- sentiment_info.append(f"Analyzed {data['headlines_count']} headlines")
- if data.get('sentiment'):
- sentiment_info.append(f"Overall sentiment: {data['sentiment']}")
-
- if sentiment_info:
- st.info("Sentiment analysis completed. " + " | ".join(sentiment_info))
- else:
- st.info("No detailed sentiment analysis data available")
-
- with tab3:
- st.markdown("### ⚙️ Agent Reasoning Process")
-
- # Check if we have any reasoning data - be more specific about what constitutes "available"
- reasoning_steps = data.get('reasoning_steps') or []
- has_reasoning_steps = len(reasoning_steps) > 0
- has_tools_used = data.get('tools_used') and len(data.get('tools_used', [])) > 0
- has_iterations = data.get('iterations') and data.get('iterations', 0) > 0
- has_source_info = data.get('source') == 'react_analysis' # Fresh analysis vs cached
-
- # For cached data, don't show reasoning even if it has generic cache steps
- is_cached = data.get('source') == 'cache'
- has_meaningful_reasoning = has_reasoning_steps and not is_cached
-
- if has_meaningful_reasoning or (has_tools_used and not is_cached) or (has_iterations and not is_cached):
- st.markdown("""
-
- 🧠 ReAct Agent Decision Process
-
- """, unsafe_allow_html=True)
-
- # Show reasoning steps if available
- if has_meaningful_reasoning:
- reasoning_data = reasoning_steps # Use the cleaned version
- st.markdown("**📋 Agent Reasoning Steps:**")
- if isinstance(reasoning_data, str):
- st.markdown(reasoning_data)
- elif isinstance(reasoning_data, list):
- for i, step in enumerate(reasoning_data, 1):
- st.markdown(f"**Step {i}:** {step}")
- else:
- st.markdown(str(reasoning_data))
-
- # Show tools used if available (but not for cached data)
- if has_tools_used and not is_cached:
- st.markdown("**🔧 Tools Used:**")
- tools = data['tools_used']
- if isinstance(tools, list):
- for tool in set(tools): # Remove duplicates
- st.markdown(f"• {tool}")
- else:
- st.markdown(str(tools))
-
- # Show iteration count if available (but not for cached data)
- if has_iterations and not is_cached:
- st.markdown(f"**🔄 Analysis Iterations:** {data['iterations']}")
-
- # Show analysis type
- if has_source_info and not is_cached:
- st.info(f"✅ Fresh ReAct analysis completed with {data.get('iterations', 0)} reasoning iterations")
-
- else:
- # Determine why no reasoning data is available
- source = data.get('source', 'unknown')
- if source == 'cache':
- st.info("📚 This is a cached result from a previous analysis. Detailed reasoning steps are not available for cached results.")
- elif not has_source_info:
- st.warning("⚠️ No detailed reasoning information available. This may be an incomplete analysis or cached result.")
- else:
- # This shouldn't happen for fresh analyses
- st.error("❌ No reasoning data available despite being a fresh analysis. This may indicate an issue with the ReAct agent.")
-
- # Display raw data for debugging
- with st.expander("🔍 Raw Analysis Data (Debug)"):
- st.json(data)
-
-
-def display_visualizations(ticker: str):
- """Displays data visualizations for price trends and sentiment distribution."""
- st.markdown("### 📊 Market Insights")
-
- col1, col2 = st.columns(2)
-
- with col1:
- st.markdown("**📈 30-Day Price Trend**")
-
- from stocksense.data_collectors import get_price_history
-
- with st.spinner("Fetching real market data..."):
- price_data = get_price_history(ticker, period="1mo")
-
- if price_data is not None and not price_data.empty:
- price_df = pd.DataFrame({'Price': price_data['Close']})
- st.line_chart(price_df, use_container_width=True)
-
- current_price = price_data['Close'].iloc[-1]
- prev_price = price_data['Close'].iloc[-2] if len(price_data) > 1 else current_price
- change_pct = ((current_price - prev_price) / prev_price) * 100 if prev_price != 0 else 0
- trend_icon = "📈" if change_pct > 0 else "📉"
-
- st.markdown(f"""
-
-
{trend_icon} Current Price (Real Data)
-
${current_price:.2f}
-
- {change_pct:+.2f}% from previous day
-
- Source: Yahoo Finance
-
- """, unsafe_allow_html=True)
- else:
- st.error(f"❌ Unable to fetch real price data for {ticker}")
- st.info("Please check if the ticker symbol is valid and try again.")
-
- with col2:
- st.markdown("**📊 Sentiment Distribution**")
-
- from stocksense.database import get_latest_analysis
-
- try:
- cached_result = get_latest_analysis(ticker)
- if cached_result and cached_result.get('sentiment_report'):
- sentiment_data = cached_result['sentiment_report']
-
- if isinstance(sentiment_data, str) and sentiment_data.strip():
- sentiment_counts = {'Positive': 0, 'Negative': 0, 'Neutral': 0}
-
- text_lower = sentiment_data.lower()
-
- lines = sentiment_data.split('\n')
- for line in lines:
- line_lower = line.lower()
- if 'sentiment:' in line_lower or 'sentiment is' in line_lower:
- if 'positive' in line_lower:
- sentiment_counts['Positive'] += 1
- elif 'negative' in line_lower:
- sentiment_counts['Negative'] += 1
- elif 'neutral' in line_lower:
- sentiment_counts['Neutral'] += 1
-
- if sum(sentiment_counts.values()) == 0:
- positive_words = ['positive', 'bullish', 'optimistic', 'strong', 'good', 'gains', 'up', 'growth', 'beat', 'exceeds']
- negative_words = ['negative', 'bearish', 'pessimistic', 'weak', 'bad', 'losses', 'down', 'decline', 'miss', 'disappoints']
- neutral_words = ['neutral', 'mixed', 'stable', 'unchanged', 'moderate']
-
- for word in positive_words:
- sentiment_counts['Positive'] += text_lower.count(word)
- for word in negative_words:
- sentiment_counts['Negative'] += text_lower.count(word)
- for word in neutral_words:
- sentiment_counts['Neutral'] += text_lower.count(word)
-
- max_count = max(sentiment_counts.values())
- if max_count > 10:
- for key in sentiment_counts:
- sentiment_counts[key] = min(sentiment_counts[key], 10)
-
- if sum(sentiment_counts.values()) == 0:
- sentiment_counts['Neutral'] = 1
-
- sentiment_df = pd.DataFrame(
- list(sentiment_counts.items()),
- columns=['Sentiment', 'Count']
- )
-
- if PLOTLY_AVAILABLE:
- colors = {
- 'Positive': '#28a745',
- 'Negative': '#dc3545',
- 'Neutral': '#6c757d'
- }
-
- fig = go.Figure(data=[
- go.Bar(
- x=sentiment_df['Sentiment'],
- y=sentiment_df['Count'],
- marker_color=[colors.get(sent, '#6c757d') for sent in sentiment_df['Sentiment']],
- text=sentiment_df['Count'],
- textposition='auto',
- )
- ])
-
- fig.update_layout(
- title="Sentiment Distribution",
- xaxis_title="Sentiment",
- yaxis_title="Count",
- showlegend=False,
- height=300,
- margin=dict(l=20, r=20, t=40, b=20)
- )
-
- st.plotly_chart(fig, use_container_width=True)
-
- else:
- st.bar_chart(sentiment_df.set_index('Sentiment'), use_container_width=True)
-
- total_headlines = sum(sentiment_counts.values())
- dominant_sentiment = max(sentiment_counts, key=sentiment_counts.get) if total_headlines > 0 else "Neutral"
-
- st.markdown(f"""
-
-
📊 Sentiment Summary
-
Analysis Available: ✅
-
Dominant: {dominant_sentiment}
- From real market data
-
- """, unsafe_allow_html=True)
- else:
- st.info("No sentiment analysis available. Run a new analysis to see sentiment data.")
- placeholder_df = pd.DataFrame({
- 'Sentiment': ['Positive', 'Negative', 'Neutral'],
- 'Count': [0, 0, 0]
- })
- st.bar_chart(placeholder_df.set_index('Sentiment'), use_container_width=True)
- else:
- st.info("No recent sentiment analysis available. Run a new analysis to see sentiment data.")
- placeholder_df = pd.DataFrame({
- 'Sentiment': ['Positive', 'Negative', 'Neutral'],
- 'Count': [0, 0, 0]
- })
- st.bar_chart(placeholder_df.set_index('Sentiment'), use_container_width=True)
- except Exception as e:
- st.warning("Unable to load sentiment data. Run a new analysis to generate sentiment insights.")
- placeholder_df = pd.DataFrame({
- 'Sentiment': ['Positive', 'Negative', 'Neutral'],
- 'Count': [0, 0, 0]
- })
- st.bar_chart(placeholder_df.set_index('Sentiment'), use_container_width=True)
-
-
-def display_key_metrics(ticker: str):
- """Displays key financial metrics."""
- st.markdown("### 💰 Key Metrics")
-
- from stocksense.data_collectors import get_price_history
-
- with st.spinner("Loading real market metrics..."):
- price_data = get_price_history(ticker, period="1mo")
-
- if price_data is not None and not price_data.empty:
- current_price = price_data['Close'].iloc[-1]
- prev_price = price_data['Close'].iloc[-2] if len(price_data) > 1 else current_price
- high_30d = price_data['High'].max()
- low_30d = price_data['Low'].min()
-
- returns = price_data['Close'].pct_change().dropna()
- volatility = returns.std() * 100 if len(returns) > 1 else 0
-
- col1, col2, col3, col4 = st.columns(4)
-
- with col1:
- change_pct = ((current_price - prev_price) / prev_price * 100) if prev_price != 0 else 0
- st.metric(
- "💵 Current Price",
- f"${current_price:.2f}",
- f"{change_pct:+.2f}%"
- )
-
- with col2:
- st.metric(
- "📊 30D High",
- f"${high_30d:.2f}",
- help="Highest price in the last 30 days"
- )
-
- with col3:
- st.metric(
- "📉 30D Low",
- f"${low_30d:.2f}",
- help="Lowest price in the last 30 days"
- )
-
- with col4:
- st.metric(
- "📈 Volatility",
- f"{volatility:.1f}%",
- help="Price volatility over the period"
- )
-
- st.caption("📊 All metrics sourced from Yahoo Finance real-time data")
- else:
- st.error(f"❌ Unable to fetch real market data for {ticker}")
- st.info("Please verify the ticker symbol and try again.")
-
-
-def clear_database_cache() -> bool:
- """Clear all cached analysis results from the database."""
- try:
- import sqlite3
- import os
-
- # Get database path (same logic as in database.py)
- current_file_dir = os.path.dirname(os.path.abspath(__file__))
- db_path = os.path.join(current_file_dir, 'stocksense.db')
-
- if os.path.exists(db_path):
- with sqlite3.connect(db_path) as conn:
- cursor = conn.cursor()
- cursor.execute('DELETE FROM analysis_cache')
- rows_deleted = cursor.rowcount
- conn.commit()
- return True, rows_deleted
- else:
- return True, 0 # No database file, so "cleared"
-
- except Exception as e:
- return False, str(e)
-
-
-def get_cache_stats() -> dict:
- """Get statistics about cached analysis results."""
- try:
- import sqlite3
- import os
-
- current_file_dir = os.path.dirname(os.path.abspath(__file__))
- db_path = os.path.join(current_file_dir, 'stocksense.db')
-
- if not os.path.exists(db_path):
- return {"total_analyses": 0, "unique_tickers": 0, "db_size_mb": 0}
-
- # Get file size
- db_size_mb = round(os.path.getsize(db_path) / (1024 * 1024), 2)
-
- with sqlite3.connect(db_path) as conn:
- cursor = conn.cursor()
-
- # Check if table exists
- cursor.execute("SELECT name FROM sqlite_master WHERE type='table' AND name='analysis_cache'")
- if not cursor.fetchone():
- return {"total_analyses": 0, "unique_tickers": 0, "db_size_mb": db_size_mb}
-
- # Get total analyses
- cursor.execute('SELECT COUNT(*) FROM analysis_cache')
- total_analyses = cursor.fetchone()[0]
-
- # Get unique tickers
- cursor.execute('SELECT COUNT(DISTINCT ticker) FROM analysis_cache')
- unique_tickers = cursor.fetchone()[0]
-
- return {
- "total_analyses": total_analyses,
- "unique_tickers": unique_tickers,
- "db_size_mb": db_size_mb
- }
-
- except Exception as e:
- return {"total_analyses": 0, "unique_tickers": 0, "db_size_mb": 0, "error": str(e)}
-
-
-def get_cached_tickers() -> list:
- """Get list of all cached ticker symbols."""
- try:
- import sqlite3
- import os
-
- current_file_dir = os.path.dirname(os.path.abspath(__file__))
- db_path = os.path.join(current_file_dir, 'stocksense.db')
-
- if not os.path.exists(db_path):
- return []
-
- with sqlite3.connect(db_path) as conn:
- cursor = conn.cursor()
-
- # Check if table exists
- cursor.execute("SELECT name FROM sqlite_master WHERE type='table' AND name='analysis_cache'")
- if not cursor.fetchone():
- return []
-
- cursor.execute('''
- SELECT DISTINCT ticker, MAX(timestamp) as latest_timestamp, COUNT(*) as analysis_count
- FROM analysis_cache
- GROUP BY ticker
- ORDER BY latest_timestamp DESC
- ''')
-
- results = cursor.fetchall()
- return [{"ticker": result[0], "latest": result[1], "count": result[2]} for result in results]
-
- except Exception as e:
- return []
-
-
-def display_analysis_history():
- """Displays analysis history in sidebar."""
- if st.session_state.analysis_history:
- st.markdown("### 📚 Recent Analyses")
-
- for i, analysis in enumerate(st.session_state.analysis_history[:5]):
- ticker = analysis['ticker']
- timestamp = analysis['timestamp']
- dt = datetime.fromisoformat(timestamp)
- time_str = dt.strftime("%m/%d %H:%M")
-
- if st.button(f"📊 {ticker} - {time_str}", key=f"history_{i}"):
- st.session_state.analysis_result = analysis
- st.rerun()
-
-
-def display_sidebar():
- with st.sidebar:
- st.markdown("### 🔧 System Status")
-
- status = check_backend_status()
- if status:
- st.markdown('
+ );
+ }
+
+ useEffect(() => {
+ // Force remount when new data arrives
+ if (analysisData) {
+ setModalKey(prev => prev + 1);
+ }
+ }, [analysisData]);
+
+ if (!analysisData || !analysisData.analysis) {
+ // If not loading and no data, show error state
+ if (!loading) {
+ return (
+