Create app.py

app.py

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+import streamlit as st
+import os
+from groq import Groq
+import fitz  # PyMuPDF for PDF parsing
+import numpy as np
+import faiss
+from sentence_transformers import SentenceTransformer  # Hugging Face transformer
+
+# Initialize the Hugging Face model and Groq API client
+model = SentenceTransformer('all-MiniLM-L6-v2')  # Model for generating embeddings
+GROQ_API_KEY = "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"
+client = Groq(api_key="GROQ_API_KEY")
+
+# Function to extract text from a PDF
+def extract_text_from_pdf(pdf_path):
+    doc = fitz.open(pdf_path)
+    text = ""
+    for page in doc:
+        text += page.get_text()
+    return text
+
+# Function to generate embeddings using Hugging Face model (for text retrieval)
+def generate_huggingface_embeddings(text):
+    embeddings = model.encode(text)  # Using the SentenceTransformer model
+    return embeddings
+
+# Function to get relevant chunks from the document using FAISS similarity search
+def get_relevant_chunks(query, top_k=5):
+    query_embedding = generate_huggingface_embeddings(query)  # Get query embedding
+    query_embedding = np.array(query_embedding).reshape(1, -1)  # Reshape for FAISS
+    
+    # Perform similarity search in FAISS
+    distances, indices = index.search(query_embedding, top_k)
+    relevant_chunks = [document_chunks[i] for i in indices[0]]
+    return relevant_chunks
+
+# Function to generate an answer based on retrieved context and Groq's model
+def generate_answer(query):
+    relevant_chunks = get_relevant_chunks(query)
+    context = " ".join(relevant_chunks)  # Combine the most relevant chunks
+
+    # Generate the response with Groq's chat model
+    chat_completion = client.chat.completions.create(
+        messages=[{"role": "user", "content": f"Answer based on this: {context}"}],
+        model="llama3-8b-8192",  # Adjust with the appropriate Groq model
+        stream=False
+    )
+    return chat_completion.choices[0].message.content
+
+# Streamlit app interface
+st.title("Knowledge-Based Assistant")
+st.write("Upload a PDF to generate answers based on its content.")
+
+# Upload PDF file
+pdf_file = st.file_uploader("Choose a PDF file", type="pdf")
+
+if pdf_file is not None:
+    # Extract the text content from the uploaded PDF
+    document_text = extract_text_from_pdf(pdf_file)
+
+    # Split the document into chunks (adjust chunk size as needed)
+    chunk_size = 1000  # Size of each chunk of text for embedding
+    document_chunks = [document_text[i:i+chunk_size] for i in range(0, len(document_text), chunk_size)]
+
+    # Generate embeddings for each chunk and store them
+    embeddings = [generate_huggingface_embeddings(chunk) for chunk in document_chunks]
+
+    # Convert embeddings to numpy arrays for FAISS
+    embeddings_array = np.array(embeddings)
+
+    # Initialize FAISS index
+    index = faiss.IndexFlatL2(embeddings_array.shape[1])  # L2 distance metric
+
+    # Add embeddings to the FAISS index
+    index.add(embeddings_array)
+
+    # Query input from user
+    query = st.text_input("Ask a question about the document:")
+
+    if query:
+        # Generate the answer based on the query
+        answer = generate_answer(query)
+        st.write("Answer: ", answer)