Update app.py

app.py

@@ -1,106 +1,68 @@
 import gradio as gr
-import requests
 from openai import OpenAI
 import os
 
-# Load your API keys from environment variables
 ACCESS_TOKEN = os.getenv("HF_TOKEN")
-TAVILY_API_KEY = os.getenv("TAVILY_API_KEY")
 print("Access token loaded.")
+
 client = OpenAI(
     base_url="https://api-inference.huggingface.co/v1/",
     api_key=ACCESS_TOKEN,
 )
 print("OpenAI client initialized.")
 
-# Define a comprehensive system prompt (global)
-SYSTEM_PROMPT = """
-You are a highly knowledgeable and reliable Crypto Trading Advisor and Analyzer. 
-Your goal is to assist users in understanding, analyzing, and making informed decisions about cryptocurrency trading. 
-"""
-
-# Binance API - Fetch Market Data
-def get_binance_data(symbol: str):
-    # Base URL for Binance API
-    url = f'https://api.binance.com/api/v3/ticker/24hr?symbol={symbol.upper()}'
-
-    try:
-        # Send GET request to Binance API
-        response = requests.get(url)
-        data = response.json()
-
-        if response.status_code != 200:
-            return {"error": "Error fetching data from Binance"}
-
-        # Extract relevant information from the API response
-        price = float(data['lastPrice'])
-        volume = float(data['volume'])
-        market_cap = float(data['quoteVolume'])  # Binance doesn't provide market cap directly, so we use quote volume as a proxy
-        change_24h = float(data['priceChangePercent'])
-
-        return {
-            'price': price,
-            'volume': volume,
-            'market_cap': market_cap,
-            'change_24h': change_24h
-        }
-    except Exception as e:
-        return {"error": f"An error occurred: {str(e)}"}
-
-# Function to handle chatbot responses
+
 def respond(
     message,
-    history: list[tuple[str, str]]
+    history: list[tuple[str, str]],
+    system_message,
+    max_tokens,
+    temperature,
+    top_p,
+    frequency_penalty,
+    seed,
+    custom_model
 ):
+
     print(f"Received message: {message}")
     print(f"History: {history}")
+    print(f"System message: {system_message}")
+    print(f"Max tokens: {max_tokens}, Temperature: {temperature}, Top-P: {top_p}")
+    print(f"Frequency Penalty: {frequency_penalty}, Seed: {seed}")
+    print(f"Selected model (custom_model): {custom_model}")
+
+    # Convert seed to None if -1 (meaning random)
+    if seed == -1:
+        seed = None
+
+    messages = [{"role": "system", "content": system_message}]
+    print("Initial messages array constructed.")
 
-    # Default values for the parameters
-    max_tokens = 512
-    temperature = 0.7
-    top_p = 0.95
-    frequency_penalty = 0.0
-    seed = None
-
-    # Check for cryptocurrency symbol in the message
-    if "crypto" in message.lower():
-        # Extract the cryptocurrency symbol from the message
-        crypto_symbol = message.split()[0].upper() + "USDT"  # Example: "Bitcoin" -> "BTCUSDT"
-        market_data = get_binance_data(crypto_symbol)
-
-        if 'error' in market_data:
-            response = "Error fetching data for this cryptocurrency."
-            yield response
-            return
-
-        # Update the system prompt with real-time data
-        updated_prompt = SYSTEM_PROMPT + f"""
-        Current Data for {crypto_symbol}:
-        - Price: ${market_data['price']}
-        - 24h Change: {market_data['change_24h']}%
-        - Volume: {market_data['volume']}
-        - Market Cap (proxy via quote volume): ${market_data['market_cap']}
-        """
-    else:
-        updated_prompt = SYSTEM_PROMPT  # Keep the original system prompt if no crypto-related message
-
-    # Prepare messages for the assistant
-    messages = [{"role": "system", "content": updated_prompt}]
+    # Add conversation history to the context
     for val in history:
         user_part = val[0]
         assistant_part = val[1]
         if user_part:
             messages.append({"role": "user", "content": user_part})
+            print(f"Added user message to context: {user_part}")
         if assistant_part:
             messages.append({"role": "assistant", "content": assistant_part})
+            print(f"Added assistant message to context: {assistant_part}")
 
-    # Add the latest user message
+    # Append the latest user message
     messages.append({"role": "user", "content": message})
+    print("Latest user message appended.")
 
-    # Start response generation
+    # If user provided a model, use that; otherwise, fall back to a default model
+    model_to_use = custom_model.strip() if custom_model.strip() != "" else "meta-llama/Llama-3.3-70B-Instruct"
+    print(f"Model selected for inference: {model_to_use}")
+
+    # Start with an empty string to build the response as tokens stream in
     response = ""
+    print("Sending request to OpenAI API.")
+
     for message_chunk in client.chat.completions.create(
-        model="meta-llama/Llama-3.3-70B-Instruct",
+        model=model_to_use,
         max_tokens=max_tokens,
         stream=True,
         temperature=temperature,
@@ -110,19 +72,132 @@ def respond(
         messages=messages,
     ):
         token_text = message_chunk.choices[0].delta.content
+        print(f"Received token: {token_text}")
         response += token_text
         yield response
 
     print("Completed response generation.")
 
-# Gradio UI
-chatbot = gr.Chatbot(height=600, show_copy_button=True, placeholder="Ask about crypto trading or analysis.", likeable=True)
+# GRADIO UI
+
+chatbot = gr.Chatbot(height=600, show_copy_button=True, placeholder="Select a model and begin chatting", likeable=True, layout="panel")
+print("Chatbot interface created.")
+
+system_message_box = gr.Textbox(value="", placeholder="You are a helpful assistant.", label="System Prompt")
+
+max_tokens_slider = gr.Slider(
+    minimum=1,
+    maximum=4096,
+    value=512,
+    step=1,
+    label="Max new tokens"
+)
+temperature_slider = gr.Slider(
+    minimum=0.1,
+    maximum=4.0,
+    value=0.7,
+    step=0.1,
+    label="Temperature"
+)
+top_p_slider = gr.Slider(
+    minimum=0.1,
+    maximum=1.0,
+    value=0.95,
+    step=0.05,
+    label="Top-P"
+)
+frequency_penalty_slider = gr.Slider(
+    minimum=-2.0,
+    maximum=2.0,
+    value=0.0,
+    step=0.1,
+    label="Frequency Penalty"
+)
+seed_slider = gr.Slider(
+    minimum=-1,
+    maximum=65535,
+    value=-1,
+    step=1,
+    label="Seed (-1 for random)"
+)
+
+# The custom_model_box is what the respond function sees as "custom_model"
+custom_model_box = gr.Textbox(
+    value="",
+    label="Custom Model",
+    info="(Optional) Provide a custom Hugging Face model path. Overrides any selected featured model.",
+    placeholder="meta-llama/Llama-3.3-70B-Instruct"
+)
+
+def set_custom_model_from_radio(selected):
+    """
+    This function will get triggered whenever someone picks a model from the 'Featured Models' radio.
+    We will update the Custom Model text box with that selection automatically.
+    """
+    print(f"Featured model selected: {selected}")
+    return selected
 
 demo = gr.ChatInterface(
     fn=respond,
+    additional_inputs=[
+        system_message_box,
+        max_tokens_slider,
+        temperature_slider,
+        top_p_slider,
+        frequency_penalty_slider,
+        seed_slider,
+        custom_model_box,
+    ],
     fill_height=True,
     chatbot=chatbot,
+    theme="Nymbo/Nymbo_Theme",
 )
+print("ChatInterface object created.")
+
+with demo:
+    with gr.Accordion("Model Selection", open=False):
+        model_search_box = gr.Textbox(
+            label="Filter Models",
+            placeholder="Search for a featured model...",
+            lines=1
+        )
+        print("Model search box created.")
+
+        models_list = [
+            "meta-llama/Llama-3.3-70B-Instruct"
+        ]
+        print("Models list initialized.")
+
+        featured_model_radio = gr.Radio(
+            label="Select a model below",
+            choices=models_list,
+            value="meta-llama/Llama-3.3-70B-Instruct",
+            interactive=True
+        )
+        print("Featured models radio button created.")
+
+        def filter_models(search_term):
+            print(f"Filtering models with search term: {search_term}")
+            filtered = [m for m in models_list if search_term.lower() in m.lower()]
+            print(f"Filtered models: {filtered}")
+            return gr.update(choices=filtered)
+
+        model_search_box.change(
+            fn=filter_models,
+            inputs=model_search_box,
+            outputs=featured_model_radio
+        )
+        print("Model search box change event linked.")
+
+        featured_model_radio.change(
+            fn=set_custom_model_from_radio,
+            inputs=featured_model_radio,
+            outputs=custom_model_box
+        )
+        print("Featured model radio button change event linked.")
+
+print("Gradio interface initialized.")
 
 if __name__ == "__main__":
-    demo.launch()
+    print("Launching the demo application.")
+    demo.launch()