Update app.py

app.py

@@ -23,7 +23,7 @@ def transcribe(audio_path):
 
     groq_api_endpoint = "https://api.groq.com/openai/v1/audio/transcriptions"
     headers = {
-        "Authorization": "Bearer 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",  # Replace with your actual API key
+        "Authorization": "Bearer YOUR_API_KEY",  # Replace with your actual API key
     }
     files = {
         'file': ('audio.wav', audio_data, 'audio/wav'),
@@ -55,11 +55,15 @@ def generate_exam_paper(transcript):
     important_sentences = get_important_sentences(sentences)
     
     # Generate exam-like questions
-    long_questions = [f"Explain the historical significance of '{sentence}'?" for sentence in important_sentences[:5]]
-    short_questions = [f"What is the definition of '{sentence.split()[0]}'?" for sentence in important_sentences[:5]]
-
+    long_questions = generate_long_questions(important_sentences)
+    short_questions = generate_short_questions(important_sentences)
     mcqs = generate_mcqs(important_sentences)
 
+    # Ensure there are exactly 2 long questions, 5 short questions, and 7 MCQs
+    long_questions = long_questions[:2]  # Limit to 2 long questions
+    short_questions = short_questions[:5]  # Limit to 5 short questions
+    mcqs = mcqs[:7]  # Limit to 7 MCQs
+
     pdf_path = create_pdf(transcript, long_questions, short_questions, mcqs)
     return pdf_path
 
@@ -70,11 +74,24 @@ def get_important_sentences(sentences):
         # Simplified heuristic: sentences with important nouns/verbs
         if len(re.findall(r'\b(NN|VB)\b', sentence)):  # Using POS tags to detect nouns/verbs
             important_sentences.append(sentence)
-    return important_sentences[:5]  # Limit to top 5 important sentences
+    return important_sentences
+
+def generate_long_questions(important_sentences):
+    long_questions = []
+    for sentence in important_sentences[:2]:  # Limit to 2 long questions
+        long_questions.append(f"Explain the historical significance of '{sentence}'?")
+    return long_questions
+
+def generate_short_questions(important_sentences):
+    short_questions = []
+    for sentence in important_sentences[:5]:  # Limit to 5 short questions
+        # Use the first word of the sentence to create short questions
+        short_questions.append(f"What is the definition of '{sentence.split()[0]}'?")
+    return short_questions
 
 def generate_mcqs(important_sentences):
     mcqs = []
-    for sentence in important_sentences:
+    for sentence in important_sentences[:7]:  # Limit to 7 MCQs
         # Generate MCQs from the sentence context
         key_terms = sentence.split()  # Simple tokenization
         correct_answer = random.choice(key_terms)  # Select a key term as the answer