update gcd

Audio Summarizer

@@ -0,0 +1,46 @@
+import whisper
+import re
+import openai
+import os
+
+def transcript_generator():
+    # Load Whisper model
+    model = whisper.load_model("base")
+
+    # Transcribe audio file
+    result = model.transcribe("audio.mp4")
+
+    # Send the transcript to the summarizer
+    provide_summarizer(result)
+
+
+def provide_summarizer(Text):
+    # Set up Groq OpenAI-compatible API credentials
+    openai.api_key = os.getenv("OPENAI_API_KEY", "your-api-key-here")  # Replace or set in environment
+    openai.api_base = "https://api.groq.com/openai/v1"
+
+    # Extract text from the Whisper result
+    text_to_summarize = Text["text"]
+
+    # Send the transcription to Groq for summarization
+    response = openai.ChatCompletion.create(
+        model="llama3-8b-8192",
+        messages=[
+            {"role": "system", "content": "You are a helpful assistant who summarizes long text into bullet points."},
+            {"role": "user", "content": f"Summarize the following:\n\n{text_to_summarize}"}
+        ]
+    )
+
+    # Split the response into sentences
+    summary = re.split(r'(?<=[.!?]) +', response["choices"][0]["message"]["content"])
+
+    # Save summary to file
+    with open("summary.txt", "w+", encoding="utf-8") as file:
+        for sentence in summary:
+            cleaned = sentence.strip()
+            if cleaned:
+                file.write("- " + cleaned + "\n")
+
+
+if __name__ == "__main__":
+    transcript_generator()

Audio_Summarizer.py

@@ -0,0 +1,46 @@
+import whisper
+import re
+import openai
+import os
+
+def transcript_generator():
+    # Load Whisper model
+    model = whisper.load_model("base")
+
+    # Transcribe audio file
+    result = model.transcribe("audio.mp4")
+
+    # Send the transcript to the summarizer
+    provide_summarizer(result)
+
+
+def provide_summarizer(Text):
+    # Set up Groq OpenAI-compatible API credentials
+    openai.api_key = os.getenv("OPENAI_API_KEY", "your-api-key-here")  # Replace or set in environment
+    openai.api_base = "https://api.groq.com/openai/v1"
+
+    # Extract text from the Whisper result
+    text_to_summarize = Text["text"]
+
+    # Send the transcription to Groq for summarization
+    response = openai.ChatCompletion.create(
+        model="llama3-8b-8192",
+        messages=[
+            {"role": "system", "content": "You are a helpful assistant who summarizes long text into bullet points."},
+            {"role": "user", "content": f"Summarize the following:\n\n{text_to_summarize}"}
+        ]
+    )
+
+    # Split the response into sentences
+    summary = re.split(r'(?<=[.!?]) +', response["choices"][0]["message"]["content"])
+
+    # Save summary to file
+    with open("summary.txt", "w+", encoding="utf-8") as file:
+        for sentence in summary:
+            cleaned = sentence.strip()
+            if cleaned:
+                file.write("- " + cleaned + "\n")
+
+
+if __name__ == "__main__":
+    transcript_generator()

String_Palindrome.py

@@ -1,15 +1,15 @@
 # Program to check if a string is palindrome or not
 
-my_str = 'aIbohPhoBiA'
+my_str = input().strip()
 
 # make it suitable for caseless comparison
 my_str = my_str.casefold()
 
 # reverse the string
-rev_str = reversed(my_str)
+rev_str = my_str[::-1]
 
 # check if the string is equal to its reverse
-if list(my_str) == list(rev_str):
+if my_str == rev_str:
    print("The string is a palindrome.")
 else:
    print("The string is not a palindrome.")

gcd.py

@@ -5,10 +5,10 @@
 a = int(input("Enter number 1 (a): "))
 b = int(input("Enter number 2 (b): "))
 
-i = 1
-while i <= a and i <= b:
-    if a % i == 0 and b % i == 0:
-        gcd = i
-    i = i + 1
+def calc_GCD(x,y):
+    if y==0:
+        return x
+    return calc_GCD(y,x%y)
+gcd=calc_GCD(a,b)
 
 print("\nGCD of {0} and {1} = {2}".format(a, b, gcd))