Add Flask app and requirements.

app.py

@@ -0,0 +1,176 @@
+from flask import Flask, request, send_file, jsonify
+import subprocess
+import numpy as np
+import ffmpeg
+import whisper
+import re
+from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline
+from io import BytesIO
+import torch
+from reportlab.lib.pagesizes import letter
+from reportlab.pdfgen import canvas
+import textwrap
+import os
+
+app = Flask(__name__)
+
+# -------------------------------
+# Global setup
+# -------------------------------
+model_name = "Qwen/Qwen2.5-7B"
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+model = AutoModelForCausalLM.from_pretrained(
+    model_name,
+    device_map="auto",        
+    torch_dtype=torch.float16 
+)
+summarizer = pipeline("summarization", model="facebook/bart-large-cnn")
+
+# -------------------------------
+# Pipeline functions
+# -------------------------------
+def stream_youtube_audio(video_url):
+    command = [
+        "yt-dlp",
+        "-f", "bestaudio",
+        "--no-playlist",
+        "-o", "-",  
+        video_url
+    ]
+    process = subprocess.Popen(command, stdout=subprocess.PIPE, stderr=subprocess.PIPE)
+    return process.stdout.read()
+
+def audio_stream_to_numpy(audio_bytes):
+    try:
+        out, _ = (
+            ffmpeg.input("pipe:0")
+            .output("pipe:1", format="wav", acodec="pcm_s16le", ac=1, ar="16000")
+            .run(input=audio_bytes, capture_stdout=True, capture_stderr=True)
+        )
+        audio_data = np.frombuffer(out, np.int16).astype(np.float32) / 32768.0
+        return audio_data
+    except ffmpeg.Error as e:
+        print("FFmpeg error:", e)
+        return None
+
+def transcribe_audio_numpy(audio_data):
+    model_whisper = whisper.load_model("tiny")
+    result = model_whisper.transcribe(audio_data)
+    print("Transcription completed.")
+    return result["text"]
+
+def summarize_text(transcription, max_tokens=512):
+    if len(transcription.split()) < 100:
+        return transcription
+    summary = summarizer(transcription, max_length=max_tokens, min_length=100, do_sample=False)
+    return summary[0]['summary_text']
+
+def generate_questionnaire(summary):
+    prompt = f"""
+You are a professional questionnaire generator reputed for generating diverse questionnaires, given any
+information sample.
+
+The questionnaire you generate must contain:
+1. Three simple multiple-choice questions (each with 4 options).
+2. One moderately difficult multiple-choice question (4 options).
+3. Two simple open-ended questions.
+4. Three moderately difficult open-ended questions.
+5. One hard scenario-based open-ended question.
+
+Make sure to cover each and every type of question mentioned.
+Nothing else, no code. Stick strictly to the provided context.
+Also, provide the questions in a structured, well-formatted, sequential manner.
+Start question sections with ### Multiple-Choice Questions etc.
+Generate a well-structured questionnaire based on the following content:
+"{summary}"
+"""
+    inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
+    outputs = model.generate(
+        **inputs,
+        max_length=2000,  
+        temperature=0.2,
+        top_p=0.8,
+        repetition_penalty=1.1,
+        do_sample=True
+    )
+    output_text = tokenizer.decode(outputs[0], skip_special_tokens=True)
+    print("Questionnaire generation completed.")
+    return clean_questionnaire(output_text)
+
+def clean_questionnaire(raw_text):
+    match = re.search(r"(### Multiple-Choice Questions.*?)$", raw_text, re.DOTALL)
+    cleaned_text = match.group(1) if match else raw_text  
+    return cleaned_text.strip()
+
+def save_text_as_pdf(text, filename):
+    c = canvas.Canvas(filename, pagesize=letter)
+    width, height = letter
+    margin = 50
+    text_object = c.beginText(margin, height - margin)
+    text_object.setFont("Helvetica", 12)
+    max_chars_per_line = 100
+
+    for paragraph in text.split("\n"):
+        wrapped_lines = textwrap.wrap(paragraph, width=max_chars_per_line)
+        if not wrapped_lines:
+            text_object.textLine("")
+        for line in wrapped_lines:
+            text_object.textLine(line)
+            if text_object.getY() < margin:
+                c.drawText(text_object)
+                c.showPage()
+                text_object = c.beginText(margin, height - margin)
+                text_object.setFont("Helvetica", 12)
+    c.drawText(text_object)
+    c.save()
+
+def process_stream(video_url, output_pdf="questionnaire.pdf"):
+    print("Streaming audio...")
+    audio_bytes = stream_youtube_audio(video_url)
+    if not audio_bytes:
+        print("Error: Unable to fetch audio.")
+        return None
+
+    print("Converting audio stream to NumPy array...")
+    audio_data = audio_stream_to_numpy(audio_bytes)
+    if audio_data is None:
+        print("Error: Unable to process audio data.")
+        return None
+
+    print("Transcribing audio...")
+    transcription = transcribe_audio_numpy(audio_data)
+    if not transcription:
+        print("Error: Transcription failed.")
+        return None
+
+    print("Summarizing transcription...")
+    summary = summarize_text(transcription)
+
+    print("Generating questionnaire...")
+    questionnaire = generate_questionnaire(summary)
+
+    print("Converting questionnaire to PDF...")
+    save_text_as_pdf(questionnaire, output_pdf)
+
+    print(f"PDF generated: {output_pdf}")
+    return output_pdf
+
+# -------------------------------
+# API endpoints
+# -------------------------------
+@app.route('/process', methods=['POST'])
+def process_video():
+    data = request.get_json()
+    video_url = data.get("video_url")
+    if not video_url:
+        return jsonify({"error": "No video URL provided."}), 400
+
+    pdf_file = process_stream(video_url)
+    if not pdf_file:
+        return jsonify({"error": "Processing failed. Check logs for details."}), 500
+
+    return send_file(pdf_file, as_attachment=True)
+
+if __name__ == '__main__':
+    # When deploying on a cloud service, make sure the port is set appropriately.
+    app.run(debug=True, use_reloader=False, host="0.0.0.0", port=5000)