Merge remote-tracking branch 'origin/main' into IOS_Calculator

Author: mary

Diff for: FLASK PROJECTS/Instagram Scraper using Flask/.gitignore

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+*.pyc
+/__pycache__
+.env

Diff for: FLASK PROJECTS/Instagram Scraper using Flask/README.md

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+<!--Please do not remove this part-->
+![Star Badge](https://img.shields.io/static/v1?label=%F0%9F%8C%9F&message=If%20Useful&style=style=flat&color=BC4E99)
+![Open Source Love](https://badges.frapsoft.com/os/v1/open-source.svg?v=103)
+
+# Instragam Profile Scraper in Flask
+
+## 🛠️ Description
+This project is about developing a an API in **Flask** using **Instaloader** and **Instagram's GraphQL API's** to scrape the no of likes and comments of all of the post of a public instagram profile.
+
+## ⚙️ Languages or Frameworks Used
+ - Flask
+ - Instaloader, Instgram GraphQL APIs
+
+
+## 🌟 How to run
+ - ### Install all the requirements
+    Run `pip install -r requirements.txt` to install all the requirements.
+
+-  ###  Now Just, Run the project
+    - To the run the project, go to the `bash` terminal of VSCode or any other code editor and run `./start_server.sh`.
+    - The server would start running on `http://127.0.0.1:{port_number}`.(generally http://127.0.0.1:5000)
+    
+ - ### Explore the API
+    Go to the browser/postman/thunderclient and hit the following URL http://127.0.0.1:5000/get_profile/{instagram_username}
+ > Note: The Instagram Profile must be **public**.
+
+
+## 📺 Demo
+- Main screen of the application.
+![image](https://github.com/MBSA-INFINITY/Python-project-Scripts/assets/85332648/0b57f8f5-aa8a-416e-9fad-e86c4e416e33)
+- Result for my instagram account (**@mbsaiaditya**).
+![image](https://github.com/MBSA-INFINITY/Python-project-Scripts/assets/85332648/2069060a-9fdf-4877-aadc-e708e5505e60)
+
+## 🤖 Author
+Github - [MBSA-INFINITY](https://github.com/MBSA-INFINITY)
+LinkedIn - [MBSAIADITYA](https://www.linkedin.com/in/mbsaiaditya/)
+Portfolio - [MBSA](https://mbsaiaditya.in/)
+Instagram - [MBSAIADITYA](https://instagram.com/mbsaiaditya)

Diff for: FLASK PROJECTS/Instagram Scraper using Flask/app.py

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+from flask import Flask, request, jsonify
+from helper import get_all_posts, scrape_user_id
+import instaloader
+
+app = Flask(__name__)
+insta = instaloader.Instaloader()
+
+@app.route('/', methods=['GET'])
+def start():
+    return "Instragram Scraper Server is Running!!"
+
+@app.route('/get_profile/<username>', methods=['GET'])
+def get_instagram_profile(username):
+    try:
+        profile = instaloader.Profile.from_username(insta.context, username)
+        #Get 
+        user_id = scrape_user_id(username)
+        # Get post data for all posts
+        post_data = get_all_posts(user_id)
+        response = {
+            "Username": profile.username,
+            "Number Of Posts": profile.mediacount,
+            "Posts": post_data
+        }
+        return jsonify(response)
+    except instaloader.exceptions.ProfileNotExistsException:
+        return jsonify({"error": "Profile does not exist"}), 404
+    except instaloader.exceptions.InstaloaderException as e:
+        return jsonify({"error": f"An error occurred: {str(e)}"}), 500
+    except Exception as e:
+        return jsonify({"error": f"{str(e)}"}), 400
+
+
+if __name__ == '__main__':
+    app.run()

Diff for: FLASK PROJECTS/Instagram Scraper using Flask/helper.py

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+import json
+import httpx
+from urllib.parse import quote
+from typing import Dict
+import jmespath
+
+client = httpx.Client(
+    headers={
+        "x-ig-app-id": "936619743392459",
+        "User-Agent": "Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/62.0.3202.94 Safari/537.36",
+        "Accept-Language": "en-US,en;q=0.9,ru;q=0.8",
+        "Accept-Encoding": "gzip, deflate, br",
+        "Accept": "*/*",
+    }
+)
+
+def scrape_user_id(username: str):
+    """Scrape Instagram user's data"""
+    result = client.get(
+        f"https://i.instagram.com/api/v1/users/web_profile_info/?username={username}",
+    )
+    data = json.loads(result.content)
+    user_data = data["data"]["user"]
+    user_id = user_data.get("id")
+    return user_id
+
+def parse_post(data: Dict) -> Dict:
+    result = jmespath.search("""{
+        shortcode: shortcode,
+        likes: edge_media_preview_like.count,
+        comments: edge_media_to_comment.count
+    }""", data)
+    return result
+
+def scrape_user_posts(user_id: str, session: httpx.Client, page_size=12):
+    base_url = "https://www.instagram.com/graphql/query/?query_hash=e769aa130647d2354c40ea6a439bfc08&variables="
+    variables = {
+        "id": user_id,
+        "first": page_size,
+        "after": None,
+    }
+    _page_number = 1
+    while True:
+        resp = session.get(base_url + quote(json.dumps(variables)))
+        all_posts_data = resp.json()
+        posts = all_posts_data["data"]["user"]["edge_owner_to_timeline_media"]
+        for post in posts.get("edges"):
+            yield parse_post(post.get("node"))  # note: we're using parse_post function from previous chapter
+        page_info = posts.get("page_info")
+        if _page_number == 1:
+            print(f"scraping total {posts['count']} posts of {user_id}")
+        else:
+            print(f"scraping page {_page_number}")
+        if not page_info["has_next_page"]:
+            break
+        if variables["after"] == page_info["end_cursor"]:
+            break
+        variables["after"] = page_info["end_cursor"]
+        _page_number += 1
+
+
+def get_all_posts(user_id):
+    with httpx.Client(timeout=None) as session:
+        posts = list(scrape_user_posts(str(user_id), session))
+        return posts

Diff for: FLASK PROJECTS/Instagram Scraper using Flask/requirements.txt

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+Flask
+instaloader
+flask-cors
+httpx 
+jmespath

Diff for: FLASK PROJECTS/Instagram Scraper using Flask/start_server.sh

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+python app.py

Diff for: MachineLearning Projects/Student-Feedback-Sentiment-Analysis-main/Student-Feedback-Sentiment-Analysis-main/Procfile

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+web: gunicorn app:app

Diff for: MachineLearning Projects/Student-Feedback-Sentiment-Analysis-main/Student-Feedback-Sentiment-Analysis-main/README.md

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+
+## PROJECT OUTLINE
+The goal of this project is to develop an automated robust feedback analysis system wherein the user has to first upload form response data in csv format, then we process and extract the data with the help of NLTK and then applying sentiment analysis using Naive Bayes algorithm to extract the opinions and subjectivity from the student feedback by classifying them into 3 different categories: 
+- positive 
+- neutral 
+- negative
+
+Nowadays, Educational Institutions are giving a lot of emphasis on maintaining Student Feedback Systems so as to understand the patterns generated by student feedback data to effectively improve the performance of the institution. There is also a requirement to automate the student feedback system in order to handle a large amount of data and analyse them effectively.
+
+Our feedback analysis model will thus enable the concerned authorities to understand the student’s  perception and their expected level of learning materials and course content. Analysing student feedback would help institutions to enhance student’s learning capabilities in the classroom and improve the overall quality of education.
+
+## WEBSITE LINK
+_https://get-analysis.herokuapp.com/_
+
+## DEMO OF WORKING MODEL 
+_[Project Demo](https://drive.google.com/file/d/1hAu_Rmdu8qq6yjq8DSgD-P08EA4nTuVu/view?usp=sharing)_ 
+
+<br>
+
+## PROJECT WORKFLOW
+![outline](images/flow.PNG)
+
+<br>
+
+## RESULTS
+![results](images/results.PNG)
+
+<br>
+
+## WEBSITE LAYOUT
+![results](images/1.png)
+
+![results](images/2.png)
+
+![results](images/3.png)
+