Create app.py

app.py

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+import streamlit as st
+import fitz  # PyMuPDF
+import faiss
+from sentence_transformers import SentenceTransformer
+import numpy as np
+from phi.agent import Agent
+from phi.model.groq import Groq
+
+# Load embedding model
+embedding_model = SentenceTransformer("all-MiniLM-L6-v2")
+
+def agent_response(question, retrieved_text):
+    agent = Agent(
+        model=Groq(id="llama-3.3-70b-versatile"),
+        markdown=True,
+        description="You are an AI assistant that provides the answer based on the provided document.",
+        instructions=[
+            f"First read the question carefully. The question is: **{question}**",
+            f"Then read the document provided to you as a text. The document is: \n**{retrieved_text}**\n",
+            "Finally answer the question based on the provided document only. Don't try to give random responses."
+        ]
+    )
+    response = agent.run(question + '\n' + retrieved_text).content
+    return response
+
+class PDFChatbot:
+    def __init__(self):
+        self.text_chunks = []
+        self.index = None
+    
+    def process_pdf(self, pdf_file):
+        """Extract text from PDF and create FAISS index."""
+        self.text_chunks = []
+        doc = fitz.open(pdf_file)
+        for page in doc:
+            self.text_chunks.append(page.get_text("text"))
+        
+        # Embed the chunks
+        embeddings = embedding_model.encode(self.text_chunks, convert_to_numpy=True)
+        
+        # Create FAISS index
+        self.index = faiss.IndexFlatL2(embeddings.shape[1])
+        self.index.add(embeddings)
+        return "PDF processed successfully!"
+    
+    def chat(self, query):
+        """Retrieve the most relevant chunk for a query."""
+        if self.index is None:
+            return "Please upload a PDF first."
+        
+        query_embedding = embedding_model.encode([query], convert_to_numpy=True)
+        _, indices = self.index.search(query_embedding, 1)  # Get top match
+        retrieved_text = self.text_chunks[indices[0][0]]
+        response = agent_response(query, retrieved_text)
+        return response
+
+# Instantiate chatbot
+chatbot = PDFChatbot()
+
+st.title("Chat with your PDF")
+
+uploaded_file = st.file_uploader("Upload your PDF", type=["pdf"])
+if uploaded_file:
+    message = chatbot.process_pdf(uploaded_file)
+    st.success(message)
+
+query = st.text_input("Ask a question")
+if st.button("Ask"):
+    if query:
+        response = chatbot.chat(query)
+        st.text_area("Answer", response, height=200)