Update app.py

app.py

@@ -1,31 +1,29 @@
 import streamlit as st
 import requests
-import json
 
-st.title("DeepSeek-R1-Distill-Qwen-32B")
-
-# The Inference API endpoint for your model
+# Hugging Face API URL
 API_URL = "https://api-inference.huggingface.co/models/deepseek-ai/DeepSeek-R1-Distill-Qwen-32B"
 
-# If your model is public, you can often omit the token.
-# If it is private or rate-limited, you need to provide a token:
-#   headers = {"Authorization": "Bearer YOUR_HF_INFERENCE_API_TOKEN"}
-headers = {}
-
-def query_hf_api(prompt: str):
-    """
-    Sends a JSON payload to the HF Inference API.
-    """
-    payload = {"inputs": prompt}
-    response = requests.post(API_URL, headers=headers, data=json.dumps(payload))
+# Function to query the Hugging Face API
+def query(payload):
+    headers = {"Authorization": f"Bearer {st.secrets['HF_TOKEN']}"}
+    response = requests.post(API_URL, headers=headers, json=payload)
     return response.json()
 
-# Simple text box for user input
-user_input = st.text_input("Enter your prompt", value="Hello, how are you?")
+# Streamlit app
+st.title("DeepSeek-R1-Distill-Qwen-32B Chatbot")
+
+# Input text box
+user_input = st.text_input("Enter your message:")
 
-# Generate button
-if st.button("Generate"):
-    with st.spinner("Generating..."):
-        result = query_hf_api(user_input)
-    st.write("**API Response:**")
-    st.json(result)
+if user_input:
+    # Query the Hugging Face API with the user input
+    payload = {"inputs": user_input}
+    output = query(payload)
+    
+    # Display the output
+    if isinstance(output, list) and len(output) > 0 and 'generated_text' in output[0]:
+        st.write("Response:")
+        st.write(output[0]['generated_text'])
+    else:
+        st.write("Error: Unable to generate a response. Please try again.")