Update app.py

app.py

@@ -45,34 +45,88 @@ def transcribe(audio_path):
         return create_error_pdf(f"API Error: {error_msg}")
 
 def generate_notes(transcript):
-    try:
-        sentences = sent_tokenize(transcript)
-    except LookupError:
-        sentences = custom_sent_tokenize(transcript)
-
-    # Generate long questions
-    long_questions = [f"Explain the concept discussed in: '{sentence}'." for sentence in sentences[:5]]
-
-    # Generate short questions
-    short_questions = [f"What does '{sentence.split()[0]}' mean in the context of this text?" for sentence in sentences[:5]]
-
-    # Generate MCQs with relevant distractors
-    mcqs = []
-    for sentence in sentences[:5]:
-        if len(sentence.split()) > 1:  # Ensure there are enough words to create meaningful options
-            key_word = sentence.split()[0]  # Use the first word as a key term
-            distractors = ["Term A", "Term B", "Term C"]  # Replace with relevant terms if needed
-            options = [key_word] + distractors
-            random.shuffle(options)  # Shuffle options for randomness
-            mcq = {
-                "question": f"What is '{key_word}' based on the context?",
-                "options": options,
-                "answer": key_word
-            }
-            mcqs.append(mcq)
+    # try:
+    #     sentences = sent_tokenize(transcript)
+    # except LookupError:
+    #     sentences = custom_sent_tokenize(transcript)
+
+    # # Generate long questions
+    # long_questions = [f"Explain the concept discussed in: '{sentence}'." for sentence in sentences[:5]]
+
+    # # Generate short questions
+    # short_questions = [f"What does '{sentence.split()[0]}' mean in the context of this text?" for sentence in sentences[:5]]
+
+    # # Generate MCQs with relevant distractors
+    # mcqs = []
+    # for sentence in sentences[:5]:
+    #     if len(sentence.split()) > 1:  # Ensure there are enough words to create meaningful options
+    #         key_word = sentence.split()[0]  # Use the first word as a key term
+    #         distractors = ["Term A", "Term B", "Term C"]  # Replace with relevant terms if needed
+    #         options = [key_word] + distractors
+    #         random.shuffle(options)  # Shuffle options for randomness
+    #         mcq = {
+    #             "question": f"What is '{key_word}' based on the context?",
+    #             "options": options,
+    #             "answer": key_word
+    #         }
+    #         mcqs.append(mcq)
+    client = Groq(api_key="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")
+
+    chat_completion = client.chat.completions.create(
+    #
+    # Required parameters
+    #
+    messages=[
+        # Set an optional system message. This sets the behavior of the
+        # assistant and can be used to provide specific instructions for
+        # how it should behave throughout the conversation.
+        {
+            "role": "system",
+            "content": "you are expert question generator from content. Generate one long question,possible number of short questions and mcqs"
+        },
+        # Set a user message for the assistant to respond to.
+        {
+            "role": "user",
+            "content": transcript,
+        }
+    ],
+
+    # The language model which will generate the completion.
+    model="llama3-8b-8192",
+
+    #
+    # Optional parameters
+    #
+
+    # Controls randomness: lowering results in less random completions.
+    # As the temperature approaches zero, the model will become deterministic
+    # and repetitive.
+    temperature=0.5,
+
+    # The maximum number of tokens to generate. Requests can use up to
+    # 32,768 tokens shared between prompt and completion.
+    max_tokens=1024,
+
+    # Controls diversity via nucleus sampling: 0.5 means half of all
+    # likelihood-weighted options are considered.
+    top_p=1,
+
+    # A stop sequence is a predefined or user-specified text string that
+    # signals an AI to stop generating content, ensuring its responses
+    # remain focused and concise. Examples include punctuation marks and
+    # markers like "[end]".
+    stop=None,
+
+    # If set, partial message deltas will be sent.
+    stream=False,
+)
+
+# Print the completion returned by the LLM.
+    res=chat_completion.choices[0].message.content
+     
 
     # Generate and save a structured PDF
-    pdf_path = create_pdf(transcript, long_questions, short_questions, mcqs)
+    pdf_path = create_pdf(res)
     return pdf_path
 
 def create_pdf(transcript, long_questions, short_questions, mcqs):