updated

app.py

@@ -1,24 +1,25 @@
-#Hello! It seems like you want to import the Streamlit library in Python. Streamlit is a powerful open-source framework used for building web applications with interactive data visualizations and machine learning models. To import Streamlit, you'll need to ensure that you have it installed in your Python environment.
-#Once you have Streamlit installed, you can import it into your Python script using the import statement,
-
 import streamlit as st
-from langchain import HuggingFaceHub  # Correct import for Hugging Face
+from langchain import HuggingFaceHub
 
 # Set your Hugging Face API token
 HUGGINGFACE_API_TOKEN = "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"
 
 # Function to return the response from Hugging Face model
 def load_answer(question):
-    # Initialize the Hugging Face model
-    llm = HuggingFaceHub(
-        repo_id="mistralai/Mistral-7B-Instruct-v0.3",  # Specify the Hugging Face model
-        huggingfacehub_api_token=HUGGINGFACE_API_TOKEN,  # Pass your API token
-        model_kwargs={"temperature": 0}  # Optional: Control response randomness
-    )
-    
-    # Call the model with the user's question and get the response
-    answer = llm(question)
-    return answer
+    try:
+        # Initialize the Hugging Face model using LangChain's HuggingFaceHub class
+        llm = HuggingFaceHub(
+            repo_id="mistralai/Mistral-7B-Instruct-v0.3",  # Hugging Face model repo
+            huggingfacehub_api_token=HUGGINGFACE_API_TOKEN,  # Pass your API token
+            model_kwargs={"temperature": 0}  # Optional: Control response randomness
+        )
+        
+        # Call the model with the user's question and get the response
+        answer = llm.run(question)
+        return answer
+    except Exception as e:
+        # Capture and return any exceptions or errors
+        return f"Error: {str(e)}"
 
 # Streamlit App UI starts here
 st.set_page_config(page_title="LangChain Demo", page_icon=":robot:")