Update main.py

main.py

@@ -0,0 +1,114 @@
+from fastapi import FastAPI, UploadFile, File  
+from fastapi.responses import HTMLResponse  
+from fastapi.staticfiles import StaticFiles  
+import os  
+from dotenv import load_dotenv  
+from PyPDF2 import PdfReader  
+from langchain.text_splitter import RecursiveCharacterTextSplitter  
+from langchain_google_genai import GoogleGenerativeAIEmbeddings  
+from langchain_community.vectorstores import FAISS  
+from langchain_google_genai import ChatGoogleGenerativeAI  
+from langchain.chains.question_answering import load_qa_chain  
+from langchain.prompts import PromptTemplate  
+import logging  
+
+app = FastAPI()  
+
+# Set up logging  
+logging.basicConfig(level=logging.INFO, format='%(asctime)s %(levelname)s: %(message)s')  
+
+load_dotenv()  
+genai.configure(api_key=os.getenv("GOOGLE_API_KEY"))  
+
+def get_pdf_text(pdf_docs):  
+    text = ""  
+    for pdf in pdf_docs:  
+        try:  
+            pdf_reader = PdfReader(pdf.file)  
+            for page in pdf_reader.pages:  
+                text += page.extract_text()  
+        except Exception as e:  
+            logging.error(f"Error processing PDF file: {e}")  
+    return text  
+
+def get_text_chunks(text):  
+    text_splitter = RecursiveCharacterTextSplitter(  
+        chunk_size=10000,  
+        chunk_overlap=1000  
+    )  
+    chunks = text_splitter.split_text(text)  
+    return chunks  
+
+def get_vector_store(text_chunks):  
+    logging.info("Starting vector store creation")  
+    embeddings = GoogleGenerativeAIEmbeddings(model="models/embedding-001")  
+    
+    vector_store = FAISS.from_texts(texts=text_chunks, embedding=embeddings)  
+    logging.info("FAISS vector store created")  
+
+    faiss_index_dir = os.path.join(os.path.dirname(__file__), "faiss_index")  
+    os.makedirs(faiss_index_dir, exist_ok=True)  
+
+    vector_store.save_local(faiss_index_dir)  
+    logging.info("FAISS vector store saved successfully.")  
+
+def get_conversation_chain():  
+    prompt_template = """  
+        Answer the question clear and precise. If not provided the context return the result as  
+        "Sorry I dont know the answer", don't provide the wrong answer.  
+        Context:\n {context}?\n  
+        Question:\n{question}\n  
+        Answer:  
+    """  
+    model = ChatGoogleGenerativeAI(model="gemini-pro", temperature=0.3)  
+    prompt = PromptTemplate(template=prompt_template, input_variables=['context', 'question'])  
+    chain = load_qa_chain(model, chain_type='stuff', prompt=prompt)  
+    return chain  
+
+def user_input(user_question):  
+    logging.info("Processing user input")  
+    
+    faiss_index_dir = os.path.join(os.path.dirname(__file__), "faiss_index")  
+    
+    if not os.path.exists(faiss_index_dir):  
+        return "Please upload and process PDF files before asking questions."  
+
+    try:  
+        new_db = FAISS.load_local(faiss_index_dir, GoogleGenerativeAIEmbeddings(model='models/embedding-001'), allow_dangerous_deserialization=True)  
+        logging.info("FAISS vector store loaded successfully")  
+        
+        docs = new_db.similarity_search(user_question)  
+        chain = get_conversation_chain()  
+        response = chain({"input_documents": docs, "question": user_question}, return_only_outputs=True)  
+        return response["output_text"]  
+    
+    except Exception as e:  
+        logging.error(f"Error processing user input: {e}")  
+        return f"Sorry, there was an error processing your request: {str(e)}. Please try again later."  
+
+@app.post("/upload_pdf/")  
+async def upload_pdf(pdf_docs: list[UploadFile] = File(...)):  
+    raw_text = get_pdf_text(pdf_docs)  
+    text_chunks = get_text_chunks(raw_text)  
+    get_vector_store(text_chunks)  
+    return {"message": "PDFs processed successfully. You can now ask questions."}  
+
+@app.get("/ask_question/")  
+async def ask_question(user_question: str):  
+    response = user_input(user_question)  
+    return {"response": response}  
+
+@app.get("/", response_class=HTMLResponse)  
+async def read_root():  
+    return """  
+        <html>  
+            <head>  
+                <title>Chat with PDFs</title>  
+            </head>  
+            <body>  
+                <h1>Welcome to Chat with PDFs API</h1>  
+                <p>Use POST /upload_pdf/ to upload PDF files.</p>  
+                <p>Use GET /ask_question/ to ask questions from the PDFs you uploaded.</p>  
+            </body>  
+        </html>  
+    """