Update app/main.py

app/main.py

@@ -12,7 +12,7 @@ from fastapi.middleware.cors import CORSMiddleware
 from fastapi import FastAPI
 from pydantic import BaseModel
 from langchain_community.embeddings import HuggingFaceBgeEmbeddings
-import nltk  # Importing NLTK
+import nltk
 import time
 
 # Set writable paths for cache and data
@@ -84,7 +84,7 @@ class Query(BaseModel):
 
 prompt = ChatPromptTemplate.from_template(
 """
-You are a helpful assistant designed specifically for the Thapar Institute of Engineering and Technology (TIET), a renowned technical college. Your task is to answer all queries related to TIET in concised manner. Every response you provide should be relevant to the context of TIET. If a question falls outside of this context, please decline by stating, 'Sorry, I cannot help with that.' If you do not know the answer to a question, do not attempt to fabricate a response; instead, politely decline.
+You are a helpful assistant designed specifically for the Thapar Institute of Engineering and Technology (TIET), a renowned technical college. Your task is to answer all queries related to TIET in a concise manner. Every response you provide should be relevant to the context of TIET. If a question falls outside of this context, please decline by stating, 'Sorry, I cannot help with that.' If you do not know the answer to a question, do not attempt to fabricate a response; instead, politely decline.
 but avoid sounding boastful or exaggerating. Stay focused on the context provided.
 If the query is not related to TIET or falls outside the context of education, respond with:
         "Sorry, I cannot help with that. I'm specifically designed to answer questions about the Thapar Institute of Engineering and Technology. 
@@ -133,7 +133,7 @@ def get_embeddings():
     model_norm = HuggingFaceBgeEmbeddings(model_name=model_name, encode_kwargs=encode_kwargs)
     return model_norm
 
-@app.post("/chat")  # Changed from /anthropic to /chat
+@app.post("/chat")
 def read_item(query: Query):
     try:
         embeddings = get_embeddings()
@@ -157,14 +157,14 @@ def read_item(query: Query):
         # 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()
 
-if _name_ == "_main_":
+if __name__ == "__main__":
     import uvicorn
-    uvicorn.run(app, host="0.0.0.0", port=8000)
+    uvicorn.run(app, host="0.0.0.0", port=8000)