Create app.py

app.py

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+import os
+import streamlit as st
+from dotenv import load_dotenv  # Importing load_dotenv to load environment variables
+from langchain import HuggingFaceHub
+
+# Load environment variables from the .env file
+load_dotenv()
+
+# Set your Hugging Face API token from the environment variable
+HUGGINGFACE_API_TOKEN = os.getenv("HUGGINGFACE_API_TOKEN")
+
+# Function to return the response from the Hugging Face model
+def load_answer(question):
+    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": 0.1}  # Set a strictly positive temperature
+        )
+        
+        # Call the model with the user's question and get the response using .predict()
+        answer = llm.predict(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="Hugging Face Demo", page_icon=":robot:")
+st.header("Hugging Face Demo")
+
+# Function to get user input
+def get_text():
+    input_text = st.text_input("You: ", key="input")
+    return input_text
+
+# Get user input
+user_input = get_text()
+
+# Create a button for generating the response
+submit = st.button('Generate')
+
+# If the generate button is clicked and user input is not empty
+if submit and user_input:
+    response = load_answer(user_input)
+    st.subheader("Answer:")
+    st.write(response)
+elif submit:
+    st.warning("Please enter a question.")  # Warning for empty input