Update app.py

app.py

@@ -1,103 +1,228 @@
-import requests
-import matplotlib.pyplot as plt
-import datetime
 import gradio as gr
 from openai import OpenAI
 import os
 
-# Coingecko API Base URL
-BASE_URL = "https://api.coingecko.com/api/v3/"
-
-# Coingecko API'den coin verilerini alma
-def get_coin_list(currency="usd"):
-    url = f"{BASE_URL}coins/markets?vs_currency={currency}&order=market_cap_desc&per_page=100&page=1&sparkline=false"
-    response = requests.get(url)
-    return response.json()
-
-def get_single_coin(id, currency="usd"):
-    url = f"{BASE_URL}coins/{id}"
-    response = requests.get(url)
-    return response.json()
-
-def get_historical_chart(id, days=365, currency="usd"):
-    url = f"{BASE_URL}coins/{id}/market_chart?vs_currency={currency}&days={days}"
-    response = requests.get(url)
-    return response.json()
-
-# OpenAI API Key ve Client Initialization
 ACCESS_TOKEN = os.getenv("HF_TOKEN")
+print("Access token loaded.")
+
 client = OpenAI(
     base_url="https://api-inference.huggingface.co/v1/",
     api_key=ACCESS_TOKEN,
 )
+print("OpenAI client initialized.")
+
+
+def respond(
+    message,
+    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.")
+
+    # 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}")
+
+    # Append the latest user message
+    messages.append({"role": "user", "content": message})
+    print("Latest user message appended.")
+
+    # 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=model_to_use,
+        max_tokens=max_tokens,
+        stream=True,
+        temperature=temperature,
+        top_p=top_p,
+        frequency_penalty=frequency_penalty,
+        seed=seed,
+        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
 
-# Hardcoded system message and other parameters
-system_message = "You are a cryptocurrency trading assistant and market analyst. Your role is to provide users with data-driven insights, technical analysis (RSI, MACD, Bollinger Bands, Moving Averages, Fibonacci retracements, volume analysis, and price action), and investment advice tailored to their risk tolerance. Focus on actionable information, such as market conditions, key indicators, and investment strategies. Avoid speculation and provide clear, concise, and unbiased recommendations based on current data."
-max_tokens = 1024
-temperature = 0.3
-top_p = 0.95
-frequency_penalty = 0.0
-seed = -1  # Random seed
-
-def respond(message, history: list[tuple[str, str]]):
-    # Coin verisini al
-    coin_data = search_coin_by_name(message)
-    if coin_data:
-        # LLM yanıtını oluştur
-        llm_msg = llm_response(coin_data, message)
-        
-        # Grafik oluştur
-        chart_image = plot_coin_chart(message)
-        
-        return llm_msg, chart_image
-    else:
-        return "Coin bulunamadı.", None
-
-def search_coin_by_name(name, currency="usd"):
-    coin_list = get_coin_list(currency)
-    for coin in coin_list:
-        if name.lower() in coin['name'].lower() or name.lower() in coin['id'].lower():
-            return coin
-    return None
-
-def llm_response(coin_data, coin_name):
-    # LLM sistem mesajı ve coin verilerini kullanarak anlamlı bir cevap üretme
-    response = f"{coin_name} ile ilgili bilgiler:\n"
-    response += f"Fiyat: {coin_data['current_price']} USD\n"
-    response += f"Piyasa Değeri: {coin_data['market_cap']} USD\n"
-    response += f"24 Saatlik Değişim: {coin_data['price_change_percentage_24h']}%\n"
-    return response
-
-def plot_coin_chart(id, days=30, currency="usd"):
-    historical_data = get_historical_chart(id, days, currency)
-    prices = historical_data['prices']
-    timestamps = [datetime.datetime.utcfromtimestamp(price[0] / 1000) for price in prices]
-    price_values = [price[1] for price in prices]
-
-    plt.figure(figsize=(10, 5))
-    plt.plot(timestamps, price_values)
-    plt.title(f"{id} Coin {days} Günlük Fiyat Grafiği")
-    plt.xlabel('Tarih')
-    plt.ylabel(f'{currency.upper()} Fiyatı')
-    plt.xticks(rotation=45)
-    plt.tight_layout()
-    plt.savefig("coin_chart.png")  # Grafiği kaydediyoruz
-    return "coin_chart.png"  # Grafiğin yolunu döndürüyoruz
-
-# Gradio UI
-chatbot = gr.Chatbot(height=600, show_copy_button=True, placeholder="Start chatting!", likeable=True, layout="panel")
+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=[],  # No additional inputs needed since everything is embedded
+    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:
-    # No need for system message input, model selection, or sliders
-    pass
+    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",
+            "meta-llama/Llama-3.1-70B-Instruct",
+            "meta-llama/Llama-3.0-70B-Instruct",
+            "meta-llama/Llama-3.2-3B-Instruct",
+            "meta-llama/Llama-3.2-1B-Instruct",
+            "meta-llama/Llama-3.1-8B-Instruct",
+            "NousResearch/Hermes-3-Llama-3.1-8B",
+            "NousResearch/Nous-Hermes-2-Mixtral-8x7B-DPO",
+            "mistralai/Mistral-Nemo-Instruct-2407",
+            "mistralai/Mixtral-8x7B-Instruct-v0.1",
+            "mistralai/Mistral-7B-Instruct-v0.3",
+            "mistralai/Mistral-7B-Instruct-v0.2",
+            "Qwen/Qwen2.5-72B-Instruct",
+            "Qwen/Qwen2.5-3B-Instruct",
+            "Qwen/Qwen2.5-0.5B-Instruct",
+            "Qwen/QwQ-32B-Preview",
+            "Qwen/Qwen2.5-Coder-32B-Instruct",
+            "microsoft/Phi-3.5-mini-instruct",
+            "microsoft/Phi-3-mini-128k-instruct",
+            "microsoft/Phi-3-mini-4k-instruct",
+            "deepseek-ai/DeepSeek-R1-Distill-Qwen-32B",
+            "deepseek-ai/DeepSeek-R1-Distill-Qwen-1.5B",
+            "HuggingFaceH4/zephyr-7b-beta",
+            "HuggingFaceTB/SmolLM2-360M-Instruct",
+            "tiiuae/falcon-7b-instruct",
+            "01-ai/Yi-1.5-34B-Chat",
+        ]
+        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()