Update app.py

app.py

@@ -1,116 +1,31 @@
-# app.py
-import os
-import requests
 import streamlit as st
-from models import get_hf_api
-
-# Configure API
-API_URL = get_hf_api()
-headers = {"Authorization": f"Bearer {st.secrets['HF_TOKEN']}"}
-
-# Page configuration
-st.set_page_config(
-    page_title="DeepSeek Chatbot - ruslanmv.com",
-    page_icon="🤖",
-    layout="centered"
-)
-
-# Initialize session state for chat history
-if "messages" not in st.session_state:
-    st.session_state.messages = []
-
-# Sidebar configuration
-with st.sidebar:
-    st.header("Model Configuration")
-    st.markdown("[Get HuggingFace Token](https://huggingface.co/settings/tokens)")
-    
-    system_message = st.text_area(
-        "System Message",
-        value="You are a friendly Chatbot created by ruslanmv.com",
-        height=100
-    )
-    
-    max_tokens = st.slider(
-        "Max Tokens",
-        1, 4000, 512
-    )
-    
-    temperature = st.slider(
-        "Temperature",
-        0.1, 4.0, 0.7
-    )
-    
-    top_p = st.slider(
-        "Top-p",
-        0.1, 1.0, 0.9
-    )
-
-# Chat interface
-st.title("🤖 DeepSeek Chatbot")
-st.caption("Powered by Hugging Face Inference API - Configure in sidebar")
-
-# Display chat history
-for message in st.session_state.messages:
-    with st.chat_message(message["role"]):
-        st.markdown(message["content"])
-
-# Handle input
-if prompt := st.chat_input("Type your message..."):
-    st.session_state.messages.append({"role": "user", "content": prompt})
-    
-    with st.chat_message("user"):
-        st.markdown(prompt)
-
-    try:
-        with st.spinner("Generating response..."):
-            full_prompt = f"{system_message}\n\nUser: {prompt}\nAssistant:"
-            
-            # Make API request
-            response = requests.post(
-                API_URL,
-                headers=headers,
-                json={
-                    "inputs": full_prompt,
-                    "parameters": {
-                        "max_new_tokens": max_tokens,
-                        "temperature": temperature,
-                        "top_p": top_p,
-                        "return_full_text": False
-                    }
-                }
-            )
-            
-            # Handle API errors
-            if response.status_code != 200:
-                error_msg = response.json().get('error', 'Unknown API error')
-                st.error(f"API Error: {error_msg}")
-                if "loading" in error_msg.lower():
-                    st.info("Please wait a moment and try again. The model might be loading.")
-                return
-            
-            # Process successful response
-            result = response.json()
-            
-            if isinstance(result, list):
-                # Handle normal response format
-                assistant_response = result[0].get('generated_text', 'No response generated')
-                
-                # Clean up response
-                if "Assistant:" in assistant_response:
-                    assistant_response = assistant_response.split("Assistant:")[-1].strip()
-                
-            elif isinstance(result, dict) and 'error' in result:
-                # Handle error format
-                st.error(f"API Error: {result['error']}")
-                return
-            else:
-                st.error("Unexpected response format from API")
-                return
-
-        with st.chat_message("assistant"):
-            st.markdown(assistant_response)
-        
-        st.session_state.messages.append({"role": "assistant", "content": assistant_response})
-
-    except Exception as e:
-        st.error(f"Application Error: {str(e)}")
+import requests
+import json
+
+st.title("DeepSeek-R1-Distill-Qwen-32B")
+
+# The Inference API endpoint for your model
+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))
+    return response.json()
+
+# Simple text box for user input
+user_input = st.text_input("Enter your prompt", value="Hello, how are you?")
+
+# Generate button
+if st.button("Generate"):
+    with st.spinner("Generating..."):
+        result = query_hf_api(user_input)
+    st.write("**API Response:**")
+    st.json(result)