Update app.py

app.py

@@ -1,5 +1,13 @@
 import gradio as gr
 import requests
+from fpdf import FPDF
+import nltk
+from nltk.tokenize import sent_tokenize
+import random
+import os
+
+# Ensure nltk resources are downloaded
+nltk.download("punkt")
 
 # Function to send audio to Groq API and get transcription
 def transcribe(audio_path):
@@ -10,38 +18,101 @@ def transcribe(audio_path):
     # Groq API endpoint for audio transcription
     groq_api_endpoint = "https://api.groq.com/openai/v1/audio/transcriptions"
 
-    # Replace 'YOUR_GROQ_API_KEY' with your actual Groq API key
     headers = {
         "Authorization": "Bearer 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",
     }
 
-    # Prepare the files and data for the request
     files = {
         'file': ('audio.wav', audio_data, 'audio/wav'),
     }
     data = {
-        'model': 'whisper-large-v3-turbo',  # Specify the model to use
-        'response_format': 'json',          # Desired response format
-        'language': 'en',                   # Language of the audio
+        'model': 'whisper-large-v3-turbo',  
+        'response_format': 'json',
+        'language': 'en',
     }
 
     # Send audio to Groq API
     response = requests.post(groq_api_endpoint, headers=headers, files=files, data=data)
 
-    # Parse response
     if response.status_code == 200:
         result = response.json()
-        return result.get("text", "No transcription available.")
+        transcript = result.get("text", "No transcription available.")
+        return generate_notes(transcript)
     else:
         return f"Error: {response.status_code}, {response.text}"
 
+# Function to generate notes and questions
+def generate_notes(transcript):
+    # Split transcript into sentences
+    sentences = sent_tokenize(transcript)
+
+    # Generate long and short questions
+    long_questions = [f"What is meant by '{sentence}'?" for sentence in sentences[:5]]
+    short_questions = [f"Define '{sentence.split()[0]}'." for sentence in sentences[:5]]
+
+    # Generate MCQs
+    mcqs = []
+    for sentence in sentences[:5]:
+        mcq = {
+            "question": f"What is '{sentence.split()[0]}'?",
+            "options": [sentence.split()[0]] + random.sample(["Option 1", "Option 2", "Option 3"], 3),
+            "answer": sentence.split()[0]
+        }
+        mcqs.append(mcq)
+
+    # Create PDF
+    pdf_path = create_pdf(transcript, long_questions, short_questions, mcqs)
+    return pdf_path
+
+# Function to create and save PDF
+def create_pdf(transcript, long_questions, short_questions, mcqs):
+    pdf = FPDF()
+    pdf.add_page()
+    
+    # Title
+    pdf.set_font("Arial", "B", 16)
+    pdf.cell(200, 10, "Transcription Notes", ln=True, align="C")
+
+    # Transcription
+    pdf.set_font("Arial", "", 12)
+    pdf.multi_cell(0, 10, f"Transcription:\n{transcript}\n\n")
+
+    # Long Questions
+    pdf.set_font("Arial", "B", 14)
+    pdf.cell(200, 10, "Long Questions", ln=True)
+    pdf.set_font("Arial", "", 12)
+    for question in long_questions:
+        pdf.multi_cell(0, 10, f"- {question}\n")
+
+    # Short Questions
+    pdf.set_font("Arial", "B", 14)
+    pdf.cell(200, 10, "Short Questions", ln=True)
+    pdf.set_font("Arial", "", 12)
+    for question in short_questions:
+        pdf.multi_cell(0, 10, f"- {question}\n")
+
+    # MCQs
+    pdf.set_font("Arial", "B", 14)
+    pdf.cell(200, 10, "Multiple Choice Questions (MCQs)", ln=True)
+    pdf.set_font("Arial", "", 12)
+    for mcq in mcqs:
+        pdf.multi_cell(0, 10, f"Q: {mcq['question']}")
+        for option in mcq["options"]:
+            pdf.multi_cell(0, 10, f"   - {option}")
+        pdf.multi_cell(0, 10, f"Answer: {mcq['answer']}\n")
+
+    # Save PDF
+    pdf_path = "/mnt/data/transcription_notes.pdf"
+    pdf.output(pdf_path)
+    
+    return pdf_path
+
 # Gradio interface
 iface = gr.Interface(
     fn=transcribe,
-    inputs=gr.Audio(type="filepath"),  # Removed 'source' parameter for compatibility
-    outputs="text",
-    title="Voice to Text Converter App",
-    
+    inputs=gr.Audio(type="filepath"),
+    outputs=gr.File(label="Download PDF with Notes and Questions"),
+    title="Voice to Text Converter and Notes Generator",
 )
 
-iface.launch()
+iface.launch()