Create app.py

app.py

@@ -0,0 +1,45 @@
+import os
+import httpx
+import streamlit as st
+
+# Retrieve API keys from environment variables
+HF_TOKEN = os.getenv("HF_TOKEN", "default_hf_token")
+
+# Initialize Hugging Face API endpoint
+HF_MODEL_URL = "https://api-inference.huggingface.co/models/Xenova/gpt-3.5-turbo"
+
+# Function to get response from Hugging Face model
+def get_response(user_query: str) -> str:
+    """Get a response from the Hugging Face model for the given user query."""
+    try:
+        headers = {"Authorization": f"Bearer {HF_TOKEN}"}
+        payload = {"inputs": user_query}
+        response = httpx.post(HF_MODEL_URL, headers=headers, json=payload)
+        response.raise_for_status()
+        result = response.json()
+        
+        # Check if result is a list and handle accordingly
+        if isinstance(result, list):
+            response_text = result[0].get("generated_text", "No response generated.")
+        else:
+            response_text = "Unexpected response format."
+        
+        return response_text
+
+    except Exception as e:
+        return f"Error: {e}"
+
+# Streamlit UI for customer support chatbot
+st.title("Customer Support Chatbot")
+
+user_query = st.text_input("Enter your query:", "")
+
+if st.button("Get Response"):
+    with st.spinner("Processing..."):
+        try:
+            # Call the get_response function
+            response = get_response(user_query)
+            st.subheader("Chatbot Response")
+            st.write(response)
+        except Exception as e:
+            st.error(f"Error fetching response: {e}")