Update app.py

app.py

@@ -1,118 +1,115 @@
-import os
-import shutil
-from fastapi import FastAPI, UploadFile, File, HTTPException, Form
-from fastapi.middleware.cors import CORSMiddleware
-from pydantic import BaseModel
-import requests
-from langchain.text_splitter import RecursiveCharacterTextSplitter
-from langchain_community.vectorstores.faiss import FAISS
-from langchain.chains.question_answering import load_qa_chain
-from langchain.prompts import PromptTemplate
-from langchain_google_genai import GoogleGenerativeAIEmbeddings, ChatGoogleGenerativeAI
-import google.generativeai as genai
-from dotenv import load_dotenv
-
-app = FastAPI()
-
-# Configure CORS
-app.add_middleware(
-    CORSMiddleware,
-    allow_origins=["*"],
-    allow_credentials=True,
-    allow_methods=["*"],
-    allow_headers=["*"],
-)
-
-# Load environment variables
-load_dotenv()
-
-# Configure Google API
-genai.configure(api_key=os.getenv("GOOGLE_API_KEY"))
-
-class QuestionInput(BaseModel):
-    question: str
-
-class UploadInput(BaseModel):
-    url: str = Form(None)
-
-def scrape_data(url):
-
-    return "scraped data"
-
-def split_text_into_chunks(text):
-    splitter = RecursiveCharacterTextSplitter(chunk_size=1000, chunk_overlap=200)
-    text_chunks = splitter.split_text(text)
-    return text_chunks
-
-def create_vector_store(chunks):
-    embeddings = GoogleGenerativeAIEmbeddings(model="models/embedding-001")
-    vector_store = FAISS.from_texts(chunks, embedding=embeddings)
-    vector_store.save_local("faiss_index")
-
-def setup_conversation_chain(template):
-    model = ChatGoogleGenerativeAI(model="gemini-pro", temperature=0.3)
-    prompt = PromptTemplate(template=template, input_variables=["context", "question"])
-    chain = load_qa_chain(model, chain_type="stuff", prompt=prompt)
-    return chain
-
-@app.post("/upload")
-async def upload_files(url: str = Form(None)):
-    try:
-        # print(url)
-        # all_text = ""
-        
-        # # Process URL
-        # if url:
-        #     # check if url is valid (request doesnt give error)
-        #     if # doesnt give error
-        #         all_text = scrape_data(url)
-        #     else:
-        #         raise HTTPException(status_code=400, detail="Invalid URL")
-
-        # if not all_text:
-        #     raise HTTPException(status_code=400, detail="No content to process")
-
-        # chunks = split_text_into_chunks(all_text)
-        # create_vector_store(chunks)
-
-        return {"message": "Content uploaded and processed successfully"}
-    except HTTPException as http_exc:
-        print(f"HTTP Exception: {http_exc.detail}")
-        raise http_exc
-    except Exception as e:
-        print(f"Unhandled Exception: {e}")
-        raise HTTPException(status_code=500, detail=str(e))
-    
-
-@app.post("/ask")
-async def ask_question(question_input: QuestionInput):
-    try:
-        embeddings = GoogleGenerativeAIEmbeddings(model="models/embedding-001")
-        indexed_data = FAISS.load_local("reviews_index", embeddings, allow_dangerous_deserialization=True)
-        docs = indexed_data.similarity_search(question_input.question)
-        
-        prompt_template = """
-        Your alias is AI Rate Professor. Your task is to provide a thorough response based on the given context, ensuring all relevant details are included. 
-        If the requested information isn't available, simply state, "answer not available in context," then answer based on your understanding, connecting with the context. 
-        Don't provide incorrect information.\n\n
-        Context: \n {context}?\n
-        Question: \n {question}\n
-        Answer:
-        """
-        
-        chain = setup_conversation_chain(prompt_template)
-        response = chain({"input_documents": docs, "question": question_input.question}, return_only_outputs=True)
-        
-        print(response["output_text"])
-        return {"answer": response["output_text"]}
-    except Exception as e:
-        raise HTTPException(status_code=500, detail=str(e))
-
-# prompt_template = """
-#         Your alias is AI Rate Professor. Your task is to provide a thorough response based on the given context, ensuring all relevant details are included. 
-#         If the requested information isn't available, simply state, "answer not available in context," then answer based on your understanding, connecting with the context. 
-#         Don't provide incorrect information.\n\n
-#         Context: \n {context}?\n
-#         Question: \n {question}\n
-#         Answer:
+import os
+import shutil
+from fastapi import FastAPI, UploadFile, File, HTTPException, Form
+from fastapi.middleware.cors import CORSMiddleware
+from pydantic import BaseModel
+import requests
+from langchain.text_splitter import RecursiveCharacterTextSplitter
+from langchain_community.vectorstores.faiss import FAISS
+from langchain.chains.question_answering import load_qa_chain
+from langchain.prompts import PromptTemplate
+from langchain_google_genai import GoogleGenerativeAIEmbeddings, ChatGoogleGenerativeAI
+import google.generativeai as genai
+from dotenv import load_dotenv
+
+app = FastAPI()
+
+# Configure CORS
+app.add_middleware(
+    CORSMiddleware,
+    allow_origins=["*"],
+    allow_credentials=True,
+    allow_methods=["*"],
+    allow_headers=["*"],
+)
+
+load_dotenv()
+genai.configure(api_key="AIzaSyCCIYW0HamEfEMyGMXhQypyYtiY4O2ixUg")
+
+class QuestionInput(BaseModel):
+    question: str
+
+class UploadInput(BaseModel):
+    url: str = Form(None)
+
+def scrape_data(url):
+
+    return "scraped data"
+
+def split_text_into_chunks(text):
+    splitter = RecursiveCharacterTextSplitter(chunk_size=1000, chunk_overlap=200)
+    text_chunks = splitter.split_text(text)
+    return text_chunks
+
+def create_vector_store(chunks):
+    embeddings = GoogleGenerativeAIEmbeddings(model="models/embedding-001")
+    vector_store = FAISS.from_texts(chunks, embedding=embeddings)
+    vector_store.save_local("faiss_index")
+
+def setup_conversation_chain(template):
+    model = ChatGoogleGenerativeAI(model="gemini-pro", temperature=0.3)
+    prompt = PromptTemplate(template=template, input_variables=["context", "question"])
+    chain = load_qa_chain(model, chain_type="stuff", prompt=prompt)
+    return chain
+
+@app.post("/upload")
+async def upload_files(url: str = Form(None)):
+    try:
+        # print(url)
+        # all_text = ""
+        
+        # # Process URL
+        # if url:
+        #     # check if url is valid (request doesnt give error)
+        #     if # doesnt give error
+        #         all_text = scrape_data(url)
+        #     else:
+        #         raise HTTPException(status_code=400, detail="Invalid URL")
+
+        # if not all_text:
+        #     raise HTTPException(status_code=400, detail="No content to process")
+
+        # chunks = split_text_into_chunks(all_text)
+        # create_vector_store(chunks)
+
+        return {"message": "Content uploaded and processed successfully"}
+    except HTTPException as http_exc:
+        print(f"HTTP Exception: {http_exc.detail}")
+        raise http_exc
+    except Exception as e:
+        print(f"Unhandled Exception: {e}")
+        raise HTTPException(status_code=500, detail=str(e))
+    
+
+@app.post("/ask")
+async def ask_question(question_input: QuestionInput):
+    try:
+        embeddings = GoogleGenerativeAIEmbeddings(model="models/embedding-001")
+        indexed_data = FAISS.load_local("reviews_index", embeddings, allow_dangerous_deserialization=True)
+        docs = indexed_data.similarity_search(question_input.question)
+        
+        prompt_template = """
+        Your alias is AI Rate Professor. Your task is to provide a thorough response based on the given context, ensuring all relevant details are included. 
+        If the requested information isn't available, simply state, "answer not available in context," then answer based on your understanding, connecting with the context. 
+        Don't provide incorrect information.\n\n
+        Context: \n {context}?\n
+        Question: \n {question}\n
+        Answer:
+        """
+        
+        chain = setup_conversation_chain(prompt_template)
+        response = chain({"input_documents": docs, "question": question_input.question}, return_only_outputs=True)
+        
+        print(response["output_text"])
+        return {"answer": response["output_text"]}
+    except Exception as e:
+        raise HTTPException(status_code=500, detail=str(e))
+
+# prompt_template = """
+#         Your alias is AI Rate Professor. Your task is to provide a thorough response based on the given context, ensuring all relevant details are included. 
+#         If the requested information isn't available, simply state, "answer not available in context," then answer based on your understanding, connecting with the context. 
+#         Don't provide incorrect information.\n\n
+#         Context: \n {context}?\n
+#         Question: \n {question}\n
+#         Answer:
 #         """