Update app.py

app.py

@@ -7,7 +7,10 @@ import tempfile
 from nltk.tokenize import sent_tokenize
 import random
 from groq import Groq
-  api_key = os.environ.get("GROQ_API_KEY")
+
+# Ensure no unexpected indentation here
+api_key = os.environ.get("GROQ_API_KEY")
+
 # Attempt to download punkt tokenizer
 try:
     nltk.download("punkt")
@@ -23,7 +26,7 @@ def transcribe(audio_path):
 
     groq_api_endpoint = "https://api.groq.com/openai/v1/audio/transcriptions"
     headers = {
-        "Authorization": f"Bearer {api_key}", # Replace with your actual API key
+        "Authorization": f"Bearer {api_key}",  # Fix: api_key is used properly
     }
     files = {
         'file': ('audio.wav', audio_data, 'audio/wav'),
@@ -46,91 +49,34 @@ 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)
-    client = Groq(api_key="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")
+    client = Groq(api_key=api_key)  # Use the api_key here
 
     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.plz also provide the notes"
-        },
-        # 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
+        messages=[
+            {
+                "role": "system",
+                "content": "you are expert question generator from content. Generate one long question, possible number of short questions and mcqs. plz also provide the notes"
+            },
+            {
+                "role": "user",
+                "content": transcript,
+            }
+        ],
+        model="llama3-8b-8192",
+        temperature=0.5,
+        max_tokens=1024,
+        top_p=1,
+        stop=None,
+        stream=False,
+    )
+
+    res = chat_completion.choices[0].message.content
      
-
     # Generate and save a structured PDF
-    pdf_path = create_pdf(res,transcript)
+    pdf_path = create_pdf(res, transcript)
     return pdf_path
 
-def create_pdf(question,transcript):
+def create_pdf(question, transcript):
     pdf = FPDF()
     pdf.add_page()
     
@@ -149,23 +95,6 @@ def create_pdf(question,transcript):
     
     pdf.multi_cell(0, 10, f"- {question.encode('latin1', 'replace').decode('latin1')}\n")
 
-    # # Add 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.encode('latin1', 'replace').decode('latin1')}\n")
-
-    # # Add 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'].encode('latin1', 'replace').decode('latin1')}")
-    #     for option in mcq["options"]:
-    #         pdf.multi_cell(0, 10, f"   - {option.encode('latin1', 'replace').decode('latin1')}")
-    #     pdf.multi_cell(0, 10, f"Answer: {mcq['answer'].encode('latin1', 'replace').decode('latin1')}\n")
-
     with tempfile.NamedTemporaryFile(delete=False, suffix=".pdf") as temp_pdf:
         pdf.output(temp_pdf.name)
         pdf_path = temp_pdf.name