Create app.py

app.py

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+import gradio as gr
+import fitz  # PyMuPDF
+import faiss
+import numpy as np
+from sentence_transformers import SentenceTransformer
+import os
+import requests
+import json
+from typing import List
+
+# Load the sentence transformer model
+embedder = SentenceTransformer("all-MiniLM-L6-v2")
+
+# Initialize FAISS index
+dimension = 384  # vector size for MiniLM-L6-v2
+index = faiss.IndexFlatL2(dimension)
+stored_chunks = []
+stored_embeddings = []
+
+# Set your Groq API key here (safe in Colab if you use secrets or input())
+GROQ_API_KEY = "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"
+LLM_MODEL = "llama3-8b-8192"
+
+def extract_text_from_pdf(pdf_file):
+    doc = fitz.open(stream=pdf_file.read(), filetype="pdf")
+    text = ""
+    for page in doc:
+        text += page.get_text()
+    return text
+
+def chunk_text(text: str, chunk_size: int = 500, overlap: int = 50) -> List[str]:
+    words = text.split()
+    chunks = []
+    for i in range(0, len(words), chunk_size - overlap):
+        chunk = words[i:i + chunk_size]
+        chunks.append(" ".join(chunk))
+    return chunks
+
+def embed_and_store(chunks):
+    global stored_chunks, stored_embeddings
+    embeddings = embedder.encode(chunks)
+    index.add(np.array(embeddings, dtype=np.float32))
+    stored_chunks.extend(chunks)
+    stored_embeddings.extend(embeddings)
+
+def query_groq(prompt):
+    url = "https://api.groq.com/openai/v1/chat/completions"
+    headers = {
+        "Authorization": f"Bearer {GROQ_API_KEY}",
+        "Content-Type": "application/json"
+    }
+
+    payload = {
+        "model": LLM_MODEL,
+        "messages": [
+            {"role": "system", "content": "You are a helpful academic supervisor helping students study uploaded research papers."},
+            {"role": "user", "content": prompt}
+        ],
+        "temperature": 0.2
+    }
+
+    response = requests.post(url, headers=headers, json=payload)
+    return response.json()["choices"][0]["message"]["content"]
+
+def retrieve_answer(user_query):
+    embedded_query = embedder.encode([user_query])
+    D, I = index.search(np.array(embedded_query, dtype=np.float32), k=3)
+    context = "\n\n".join([stored_chunks[i] for i in I[0]])
+    prompt = f"Based on the following context:\n\n{context}\n\nAnswer this question:\n{user_query}"
+    return query_groq(prompt)
+
+def handle_upload(file):
+    text = extract_text_from_pdf(file)
+    chunks = chunk_text(text)
+    embed_and_store(chunks)
+    return "PDF processed and indexed. You can now ask questions."
+
+def handle_question(question):
+    if not stored_chunks:
+        return "Please upload a PDF first."
+    return retrieve_answer(question)
+
+with gr.Blocks() as demo:
+    with gr.Row():
+        file_input = gr.File(label="Upload your PDF")
+        upload_btn = gr.Button("Process PDF")
+    output_text = gr.Textbox(label="Status / Answer")
+
+    upload_btn.click(fn=handle_upload, inputs=file_input, outputs=output_text)
+
+    with gr.Row():
+        query_input = gr.Textbox(label="Ask a Question")
+        query_btn = gr.Button("Submit")
+    query_btn.click(fn=handle_question, inputs=query_input, outputs=output_text)
+
+demo.launch()