Create app.py

app.py

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+import os
+from PyPDF2 import PdfReader
+from sentence_transformers import SentenceTransformer
+import faiss
+import torch
+from transformers import pipeline
+import gradio as gr
+
+# Load models
+embedder = SentenceTransformer("all-MiniLM-L6-v2")
+model_name = "mistralai/Mistral-7B-Instruct-v0.2"  # Replace with your preferred HF model
+generator = pipeline("text-generation", model=model_name, device=0 if torch.cuda.is_available() else -1)
+
+# Globals
+texts = []
+index = None
+
+def process_pdf(file):
+    global texts, index
+    reader = PdfReader(file.name)
+    full_text = ""
+    for page in reader.pages:
+        full_text += page.extract_text() + "\n"
+
+    chunks = [full_text[i:i+500] for i in range(0, len(full_text), 500)]
+    texts = chunks
+
+    embeddings = embedder.encode(chunks)
+    index = faiss.IndexFlatL2(len(embeddings[0]))
+    index.add(embeddings)
+
+    return "PDF processed. Ask me anything about it!"
+
+def chat_fn(message, history):
+    if index is None or not texts:
+        return "Please upload and process a PDF first."
+
+    q_embedding = embedder.encode([message])
+    D, I = index.search(q_embedding, k=3)
+    context = "\n".join([texts[i] for i in I[0]])
+
+    prompt = f"""You are a helpful assistant. Use the context to answer the question.
+
+Context:
+{context}
+
+Question:
+{message}
+
+Answer:"""
+
+    output = generator(prompt, max_new_tokens=300, do_sample=True)[0]["generated_text"]
+    answer = output.split("Answer:")[-1].strip()
+    return answer
+
+with gr.Blocks() as demo:
+    gr.Markdown("## 🧠 PDF ChatBot - Ask Anything from Your Document")
+
+    with gr.Row():
+        file = gr.File(file_types=[".pdf"], label="Upload PDF")
+        status = gr.Textbox(label="Status", interactive=False)
+        upload_btn = gr.Button("Process PDF")
+
+    upload_btn.click(fn=process_pdf, inputs=file, outputs=status)
+
+    chatbot = gr.ChatInterface(chat_fn)
+
+demo.launch()