Update app.py

app.py

@@ -14,6 +14,7 @@ from langchain.prompts import PromptTemplate
 import faiss
 import uuid
 from dotenv import load_dotenv
+import requests
 
 # Load environment variables
 load_dotenv()
@@ -85,13 +86,16 @@ def process_input(input_data):
 # Question-answering logic
 def answer_question(vectorstore, query):
     if not HUGGINGFACEHUB_API_TOKEN:
-        raise RuntimeError("Missing Hugging Face API token. Please set it in your secrets.")
+        raise RuntimeError("Missing Hugging Face API token. Please set it in your .env file.")
     
-    llm = HuggingFaceHub(
-        repo_id="mistralai/Mistral-7B-Instruct-v0.1",
-        model_kwargs={"temperature": 0.7, "max_length": 512},
-        huggingfacehub_api_token=HUGGINGFACEHUB_API_TOKEN
-    )
+    try:
+        llm = HuggingFaceHub(
+            repo_id="mistralai/Mixtral-8x7B-Instruct-v0.1",
+            model_kwargs={"temperature": 0.7, "max_length": 512},
+            huggingfacehub_api_token=HUGGINGFACEHUB_API_TOKEN
+        )
+    except requests.exceptions.HTTPError as e:
+        raise RuntimeError(f"Failed to initialize LLM: {str(e)}. Check model availability or API token.")
     
     retriever = vectorstore.as_retriever(search_kwargs={"k": 3})
     prompt_template = PromptTemplate(
@@ -107,8 +111,11 @@ def answer_question(vectorstore, query):
         chain_type_kwargs={"prompt": prompt_template}
     )
     
-    result = qa_chain({"query": query})
-    return result["result"].split("Answer:")[-1].strip()
+    try:
+        result = qa_chain({"query": query})
+        return result["result"].split("Answer:")[-1].strip()
+    except requests.exceptions.HTTPError as e:
+        raise RuntimeError(f"Error querying LLM: {str(e)}. Please try again or check model endpoint.")
 
 # Sidebar with BSNL logo and authentication
 with st.sidebar: