Update app.py

app.py

@@ -1,50 +1,38 @@
 import os
 import re
-from langchain_anthropic import ChatAnthropic
+from fastapi import FastAPI, Request
+from fastapi.responses import HTMLResponse
+from fastapi.staticfiles import StaticFiles
+from fastapi.templating import Jinja2Templates
+from pydantic import BaseModel
+from langchain.chat_models import ChatAnthropic
 from langchain.text_splitter import RecursiveCharacterTextSplitter
 from langchain.chains.combine_documents import create_stuff_documents_chain
-from langchain_core.prompts import ChatPromptTemplate
+from langchain.prompts import ChatPromptTemplate
 from langchain.chains import create_retrieval_chain
-from langchain_community.vectorstores import FAISS
-from langchain_community.document_loaders import TextLoader
-from fastapi.middleware.cors import CORSMiddleware
-from fastapi import FastAPI
-from pydantic import BaseModel
-from langchain_community.embeddings import HuggingFaceBgeEmbeddings
+from langchain.vectorstores import FAISS
+from langchain.document_loaders import TextLoader
+from langchain.embeddings import HuggingFaceBgeEmbeddings
 import time
 
+app = FastAPI()
+
+# Mount static files
+app.mount("/static", StaticFiles(directory="static"), name="static")
+
+# Set up Jinja2 templates
+templates = Jinja2Templates(directory="templates")
+
 def clean_response(response):
-    # Remove any leading/trailing whitespace, including newlines
     cleaned = response.strip()
-    
-    # Remove any enclosing quotation marks
     cleaned = re.sub(r'^["\']+|["\']+$', '', cleaned)
-    
-    # Replace multiple newlines with a single newline
     cleaned = re.sub(r'\n+', '\n', cleaned)
-    
-    # Remove any remaining '\n' characters
     cleaned = cleaned.replace('\\n', '')
-    
     return cleaned
 
-app = FastAPI()
-
-app.add_middleware(
-    CORSMiddleware,
-    allow_origins=["*"],
-    allow_credentials=True,
-    allow_methods=["*"],
-    allow_headers=["*"],
-)
-
 anthropic_api_key = os.environ.get('ANTHROPIC_API_KEY')
 llm = ChatAnthropic(anthropic_api_key=anthropic_api_key, model_name="claude-3-sonnet-20240229")
 
-@app.get("/")
-def read_root():
-    return {"Hello": "World"}
-
 class Query(BaseModel):
     query_text: str
 
@@ -64,7 +52,7 @@ Question: {input}
 
 def vector_embedding():
     try:
-        file_path = "./data.txt"
+        file_path = "data.txt"
         if not os.path.exists(file_path):
             print(f"The file {file_path} does not exist.")
             return {"response": "Error: Data file not found"}
@@ -84,7 +72,7 @@ def vector_embedding():
         model_norm = HuggingFaceBgeEmbeddings(model_name=model_name, encode_kwargs=encode_kwargs)
         
         db = FAISS.from_documents(chunks, model_norm)
-        db.save_local("./vectors_db")
+        db.save_local("vectors_db")
         
         print("Vector store created and saved successfully.")
         return {"response": "Vector Store DB Is Ready"}
@@ -99,11 +87,15 @@ def get_embeddings():
     model_norm = HuggingFaceBgeEmbeddings(model_name=model_name, encode_kwargs=encode_kwargs)
     return model_norm
 
+@app.get("/", response_class=HTMLResponse)
+async def read_root(request: Request):
+    return templates.TemplateResponse("index.html", {"request": request})
+
 @app.post("/anthropic")
 def read_item(query: Query):
     try:
         embeddings = get_embeddings()
-        vectors = FAISS.load_local("./vectors_db", embeddings, allow_dangerous_deserialization=True)
+        vectors = FAISS.load_local("vectors_db", embeddings, allow_dangerous_deserialization=True)
     except Exception as e:
         print(f"Error loading vector store: {str(e)}")
         return {"response": "Vector Store Not Found or Error Loading. Please run /setup first."}
@@ -117,16 +109,18 @@ def read_item(query: Query):
         response = retrieval_chain.invoke({'input': prompt1})
         print("Response time:", time.process_time() - start)
         
-        # Apply the cleaning function to the response
         cleaned_response = clean_response(response['answer'])
         
-        # For debugging, print the cleaned response
         print("Cleaned response:", repr(cleaned_response))
         
-        return cleaned_response
+        return {"response": cleaned_response}
     else:
-        return "No Query Found"
+        return {"response": "No Query Found"}
 
 @app.get("/setup")
 def setup():
-    return vector_embedding()
+    return vector_embedding()
+
+if __name__ == "__main__":
+    import uvicorn
+    uvicorn.run(app, host="0.0.0.0", port=7860)