Create app.py

app.py

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+import streamlit as st
+import pandas as pd
+from sentence_transformers import SentenceTransformer
+import faiss
+import numpy as np
+import requests
+import fitz  # PyMuPDF for PDF text extraction
+
+# Load your data from PDF
+@st.cache_resource
+def load_data(file):
+    text = ""
+    with fitz.open(stream=file.read(), filetype="pdf") as pdf_file:
+        for page in pdf_file:
+            text += page.get_text()
+    return pd.DataFrame({'combined_text': [text]})
+
+# Initialize the embedding model and FAISS index
+@st.cache_resource
+def initialize_embeddings(data):
+    embedder = SentenceTransformer('all-MiniLM-L6-v2')
+    embeddings = embedder.encode(data['combined_text'].tolist(), convert_to_tensor=False)
+    embedding_dim = embeddings[0].shape[0]
+    index = faiss.IndexFlatL2(embedding_dim)
+    index.add(np.array(embeddings))
+    return embedder, index
+
+# OpenAI API setup
+openai_api_key = "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"  # Replace with your actual OpenAI API key
+
+# Function to retrieve top-k similar documents from FAISS index
+def retrieve(query, embedder, index, data, top_k=5):
+    query_embedding = embedder.encode([query], convert_to_tensor=False)
+    distances, indices = index.search(np.array(query_embedding), top_k)
+    return data.iloc[indices[0]]
+
+# Function for RAG using OpenAI API
+def rag_query(query, embedder, index, data, top_k=5):
+    retrieved_docs = retrieve(query, embedder, index, data, top_k)
+    context = "\n".join(retrieved_docs['combined_text'].tolist())
+    prompt = f"Context: {context}\nQuestion: {query}\nAnswer:"
+
+    # Call the OpenAI API
+    headers = {
+        "Authorization": f"Bearer {openai_api_key}",
+        "Content-Type": "application/json"
+    }
+    
+    data = {
+        "model": "gpt-3.5-turbo",  # Change to your preferred model
+        "messages": [{"role": "user", "content": prompt}],
+        "max_tokens": 100,
+        "temperature": 0.7
+    }
+    
+    response = requests.post("https://api.openai.com/v1/chat/completions", headers=headers, json=data)
+    
+    if response.status_code == 200:
+        answer = response.json().get("choices", [{}])[0].get("message", {}).get("content", "No answer found.")
+    else:
+        answer = f"Error: {response.json().get('error', 'Unknown error')}"
+
+    return answer
+
+# Streamlit UI
+st.title("RAG Application with OpenAI API")
+st.write("Ask a question, and I'll find the answer for you!")
+
+# File uploader for PDF data
+uploaded_file = st.file_uploader("Upload your PDF file", type=["pdf"])
+
+if uploaded_file is not None:
+    # Load data
+    data = load_data(uploaded_file)
+    
+    # Initialize embeddings and FAISS index
+    embedder, index = initialize_embeddings(data)
+    
+    # User input for query
+    query = st.text_input("Your question:")
+    
+    if st.button("Get Answer"):
+        if query:
+            answer = rag_query(query, embedder, index, data)
+            st.write("Answer:", answer)
+        else:
+            st.write("Please enter a question.")
+else:
+    st.write("Please upload a PDF file to start.")