Create app-working-v2.py

app-working-v2.py

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+
+import streamlit as st
+import requests
+import logging
+
+# Configure logging
+logging.basicConfig(level=logging.INFO)
+logger = logging.getLogger(__name__)
+
+# 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)")
+
+    # Dropdown to select model
+    model_options = [
+        "deepseek-ai/DeepSeek-R1-Distill-Qwen-32B",
+     #   "deepseek-ai/DeepSeek-R1-Distill-Qwen-14B",
+     #   "deepseek-ai/DeepSeek-R1-Distill-Qwen-7B",
+     #   "deepseek-ai/DeepSeek-R1-Distill-Llama-70B", 
+     #   "deepseek-ai/DeepSeek-R1-Distill-Llama-8B"
+    ]
+    selected_model = st.selectbox("Select Model", model_options, index=0)
+
+    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
+    )
+
+# Function to query the Hugging Face API
+def query(payload, api_url):
+    headers = {"Authorization": f"Bearer {st.secrets['HF_TOKEN']}"}
+    logger.info(f"Sending request to {api_url} with payload: {payload}")
+    response = requests.post(api_url, headers=headers, json=payload)
+    logger.info(f"Received response: {response.status_code}, {response.text}")
+    return response.json()
+
+# 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..."):
+            # Prepare the payload for the API
+            payload = {
+                "inputs": prompt,
+                "parameters": {
+                    "max_new_tokens": max_tokens,
+                    "temperature": temperature,
+                    "top_p": top_p,
+                    "return_full_text": False
+                }
+            }
+
+            # Dynamically construct the API URL based on the selected model
+            api_url = f"https://api-inference.huggingface.co/models/{selected_model}"
+            logger.info(f"Selected model: {selected_model}, API URL: {api_url}")
+
+            # Query the Hugging Face API using the selected model
+            output = query(payload, api_url)
+
+            # Handle API response
+            if isinstance(output, list) and len(output) > 0 and 'generated_text' in output[0]:
+                assistant_response = output[0]['generated_text']
+                logger.info(f"Generated response: {assistant_response}")
+
+                with st.chat_message("assistant"):
+                    st.markdown(assistant_response)
+
+                st.session_state.messages.append({"role": "assistant", "content": assistant_response})
+            else:
+                logger.error(f"Unexpected API response: {output}")
+                st.error("Error: Unable to generate a response. Please try again.")
+
+    except Exception as e:
+        logger.error(f"Application Error: {str(e)}", exc_info=True)
+        st.error(f"Application Error: {str(e)}")