Create app.py

app.py

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+# app.py
+import os
+import streamlit as st
+from langchain.document_loaders import PyPDFLoader
+from langchain.embeddings import HuggingFaceEmbeddings
+from langchain.vectorstores import FAISS
+from langchain.chains import RetrievalQA
+from langchain.llms import HuggingFacePipeline
+from transformers import pipeline
+from groq import Groq
+import requests
+from PyPDF2 import PdfReader
+import io
+
+# Set up API key for Groq API
+GROQ_API_KEY = "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"
+os.environ["GROQ_API_KEY"] = GROQ_API_KEY
+
+# Initialize Groq API client
+client = Groq(api_key=GROQ_API_KEY)
+
+# Predefined PDF link
+pdf_url = "https://drive.google.com/file/d/1P9InkDWyaybb8jR_xS4f4KsxTlYip8RA/view?usp=drive_link"
+
+def extract_text_from_pdf(pdf_url):
+    """Extract text from a PDF file given its Google Drive shared link."""
+    # Extract file ID from the Google Drive link
+    file_id = pdf_url.split('/d/')[1].split('/view')[0]
+    download_url = f"https://drive.google.com/uc?export=download&id={file_id}"
+    response = requests.get(download_url)
+
+    if response.status_code == 200:
+        pdf_content = io.BytesIO(response.content)
+        reader = PdfReader(pdf_content)
+        text = "\n".join([page.extract_text() for page in reader.pages])
+        return text
+    else:
+        st.error("Failed to download PDF.")
+        return ""
+
+# Streamlit Interface
+st.title("ASD Diagnosis Retrieval-Augmented Generation App")
+
+st.info("Processing predefined PDF...")
+extracted_text = extract_text_from_pdf(pdf_url)
+
+if extracted_text:
+    st.success("Text extraction complete.")
+
+    # Preprocess text for embeddings
+    st.info("Generating embeddings...")
+    embeddings_model = HuggingFaceEmbeddings(model_name="sentence-transformers/all-MiniLM-L6-v2")
+    embeddings = embeddings_model.embed_documents([extracted_text])
+
+    # Store embeddings in FAISS
+    st.info("Storing embeddings in FAISS...")
+    faiss_index = FAISS.from_texts([extracted_text], embeddings_model)
+
+    # Set up Hugging Face LLM pipeline
+    st.info("Setting up RAG pipeline...")
+    hf_pipeline = pipeline("text-generation", model="google/flan-t5-base", tokenizer="google/flan-t5-base")
+    llm = HuggingFacePipeline(pipeline=hf_pipeline)
+
+    retriever = faiss_index.as_retriever()
+    qa_chain = RetrievalQA.from_chain_type(llm=llm, chain_type="stuff", retriever=retriever)
+
+    # Query interface
+    st.success("RAG pipeline ready.")
+    user_query = st.text_input("Enter your query about ASD:")
+
+    if user_query:
+        st.info("Fetching response...")
+        response = qa_chain.run(user_query)
+        st.success(response)
+else:
+    st.error("No text extracted from the PDF.")