Update app.py

app.py

@@ -1,10 +1,19 @@
 import gradio as gr
 import requests
-import json
-import pandas as pd
-import plotly.graph_objects as go
+import torch
+import re
+from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline
 
 MARKET_CONTEXT = "Market data is loading..."
+MODEL_ID = "Qwen/Qwen2.5-1.5B-Instruct"
+
+device = "cuda" if torch.cuda.is_available() else "cpu"
+tokenizer = AutoTokenizer.from_pretrained(MODEL_ID)
+model = AutoModelForCausalLM.from_pretrained(
+    MODEL_ID,
+    torch_dtype="auto",
+    device_map="auto"
+)
 
 def fetch_crypto_data():
     url = "https://api.coingecko.com/api/v3/coins/markets"
@@ -15,7 +24,7 @@ def fetch_crypto_data():
         "per_page": 4,
         "page": 1,
         "sparkline": "true",
-        "price_change_percentage": "24h,7d"
+        "price_change_percentage": "24h"
     }
     
     global MARKET_CONTEXT
@@ -32,15 +41,14 @@ def fetch_crypto_data():
         for coin in data:
             symbol = coin['symbol'].upper()
             price = coin['current_price']
-            chg_24 = coin.get('price_change_percentage_24h_in_currency', 0) or 0
-            chg_7d = coin.get('price_change_percentage_7d_in_currency', 0) or 0
+            chg_24 = coin.get('price_change_percentage_24h', 0) or 0
             mcap = coin['market_cap'] or 0
             history = coin.get('sparkline_in_7d', {}).get('price', [])
             
-            context_parts.append(f"[{symbol}: ${price}, 24h:{chg_24:.1f}%, 7d:{chg_7d:.1f}%]")
+            context_parts.append(f"[{symbol}: ${price}, 24h:{chg_24:.1f}%]")
             processed_data.append({
                 "name": coin['name'], "symbol": symbol, "price": price,
-                "chg_24": chg_24, "chg_7d": chg_7d, "mcap": mcap, "history": history
+                "chg_24": chg_24, "mcap": mcap, "history": history
             })
             
         MARKET_CONTEXT = " | ".join(context_parts)
@@ -48,38 +56,63 @@ def fetch_crypto_data():
     except Exception:
         return None
 
+def chat_logic(user_input, history):
+    fetch_crypto_data()
+    
+    system_prompt = f"You are a professional Crypto Assistant. LIVE DATA: {MARKET_CONTEXT}. Answer concisely."
+    
+    messages = [{"role": "system", "content": system_prompt}]
+    for human, assistant in history:
+        messages.append({"role": "user", "content": human})
+        messages.append({"role": "assistant", "content": assistant})
+    messages.append({"role": "user", "content": user_input})
+
+    text = tokenizer.apply_chat_template(
+        messages,
+        tokenize=False,
+        add_generation_prompt=True
+    )
+    
+    model_inputs = tokenizer([text], return_tensors="pt").to(model.device)
+    
+    generated_ids = model.generate(
+        **model_inputs,
+        max_new_tokens=512,
+        do_sample=True,
+        temperature=0.7
+    )
+    
+    response_ids = [
+        output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
+    ]
+    
+    response = tokenizer.batch_decode(response_ids, skip_special_tokens=True)[0]
+    
+    cleaned_response = re.sub(r'<think>.*?</think>\s*\n?', '', response, flags=re.DOTALL).strip()
+    return cleaned_response
+
+import plotly.graph_objects as go
+
 def create_sparkline(history, chg_24):
     color = "#10B981" if chg_24 >= 0 else "#EF4444"
     fig = go.Figure()
-    fig.add_trace(go.Scatter(
-        y=history, mode='lines', fill='tozeroy',
-        line=dict(color=color, width=2),
-        fillcolor=f"rgba({int(color[1:3], 16)}, {int(color[3:5], 16)}, {int(color[5:7], 16)}, 0.1)"
-    ))
+    fig.add_trace(go.Scatter(y=history, mode='lines', fill='tozeroy', line=dict(color=color, width=2)))
     fig.update_layout(
         template="plotly_dark", paper_bgcolor='rgba(0,0,0,0)', plot_bgcolor='rgba(0,0,0,0)',
-        margin=dict(l=0, r=0, t=10, b=0), xaxis=dict(visible=False), yaxis=dict(visible=False),
-        showlegend=False, height=80,
+        margin=dict(l=0, r=0, t=0, b=0), xaxis=dict(visible=False), yaxis=dict(visible=False),
+        showlegend=False, height=60
     )
     return fig
 
 def create_card_html(coin):
-    if not coin: return "<div style='color:white;'>Error Loading</div>"
     color_24 = "#10B981" if coin['chg_24'] >= 0 else "#EF4444"
-    arrow = "▲" if coin['chg_24'] >= 0 else "▼"
     return f"""
-    <div style="background-color: #1F2937; padding: 20px; border-radius: 12px; border: 1px solid #374151;">
-        <div style="display: flex; justify-content: space-between; align-items: center; margin-bottom: 10px;">
-            <div style="display: flex; align-items: baseline; gap: 8px;">
-                <h2 style="margin: 0; font-size: 1.25rem; color: #F3F4F6;">{coin['name']}</h2>
-                <span style="color: #9CA3AF;">{coin['symbol']}</span>
-            </div>
-            <span style="font-size: 1.25rem; color: #F3F4F6;">${coin['price']:,.2f}</span>
-        </div>
-        <div style="display: flex; justify-content: space-between; font-size: 0.875rem;">
-            <div style="color: {color_24}; font-weight: 600;">{arrow} {coin['chg_24']:.2f}%</div>
-            <div style="color: #9CA3AF;">MCap: ${coin['mcap']/1e9:.1f}B</div>
+    <div style="background-color: #1F2937; padding: 15px; border-radius: 10px; border: 1px solid #374151; color: white;">
+        <div style="display: flex; justify-content: space-between;">
+            <b>{coin['name']} ({coin['symbol']})</b>
+            <span>${coin['price']:,.2f}</span>
         </div>
+        <div style="color: {color_24}; font-size: 0.8em;">{coin['chg_24']:.2f}% (24h)</div>
     </div>
     """
 
@@ -92,59 +125,25 @@ def refresh_dashboard():
         outputs.append(create_sparkline(coin['history'], coin['chg_24']))
     return outputs
 
-def chat_logic(message, history):
-    API_URL = "https://api-inference.huggingface.co/models/Qwen/Qwen2.5-7B-Instruct"
-    
-    system_prompt = f"You are a professional Crypto Dashboard Assistant. LIVE MARKET DATA: {MARKET_CONTEXT}"
-    payload_messages = [{"role": "system", "content": system_prompt}]
-    for human, assistant in history:
-        payload_messages.append({"role": "user", "content": human})
-        payload_messages.append({"role": "assistant", "content": assistant})
-    payload_messages.append({"role": "user", "content": message})
+with gr.Blocks(theme=gr.themes.Soft()) as demo:
+    gr.HTML("<h1 style='text-align: center;'>⚡ Local Qwen CryptoDash</h1>")
     
-    payload = {
-        "inputs": message, # Simple input format for public API requests
-        "parameters": {"max_new_tokens": 512, "temperature": 0.7},
-    }
-    
-    try:
-        # Standard request without Authorization header
-        response = requests.post(API_URL, json=payload, timeout=20)
-        if response.status_code == 200:
-            result = response.json()
-            # Handle different return formats from HF API
-            if isinstance(result, list) and "generated_text" in result[0]:
-                yield result[0]["generated_text"]
-            elif isinstance(result, dict) and "generated_text" in result:
-                yield result["generated_text"]
-            else:
-                yield str(result)
-        elif response.status_code == 429:
-            yield "⚠️ Rate limit reached (Public requests are limited). Try again in a moment."
-        else:
-            yield f"⚠️ API Error: {response.status_code}"
-    except Exception as e:
-        yield f"⚠️ Connection Error: {str(e)}"
-
-custom_css = "body { background-color: #111827; } .contain { max-width: 1400px; margin: auto; padding-top: 20px; }"
-
-with gr.Blocks(css=custom_css, theme=gr.themes.Soft(primary_hue="indigo", neutral_hue="slate")) as demo:
-    gr.HTML("<h1 style='color: white; text-align: center;'>⚡ CryptoDash AI (Qwen 7B)</h1>")
     with gr.Row():
         with gr.Column():
-            c1_html, c1_plot = gr.HTML(), gr.Plot(show_label=False, container=False)
+            c1_h = gr.HTML(); c1_p = gr.Plot(container=False)
         with gr.Column():
-            c2_html, c2_plot = gr.HTML(), gr.Plot(show_label=False, container=False)
+            c2_h = gr.HTML(); c2_p = gr.Plot(container=False)
         with gr.Column():
-            c3_html, c3_plot = gr.HTML(), gr.Plot(show_label=False, container=False)
+            c3_h = gr.HTML(); c3_p = gr.Plot(container=False)
         with gr.Column():
-            c4_html, c4_plot = gr.HTML(), gr.Plot(show_label=False, container=False)
+            c4_h = gr.HTML(); c4_p = gr.Plot(container=False)
 
-    btn_refresh = gr.Button("🔄 Refresh Market Data", variant="secondary")
-    gr.ChatInterface(fn=chat_logic, examples=["What is the price of Bitcoin?"])
+    btn = gr.Button("Update Market")
+    
+    gr.ChatInterface(fn=chat_logic)
 
-    demo.load(fn=refresh_dashboard, outputs=[c1_html, c1_plot, c2_html, c2_plot, c3_html, c3_plot, c4_html, c4_plot])
-    btn_refresh.click(fn=refresh_dashboard, outputs=[c1_html, c1_plot, c2_html, c2_plot, c3_html, c3_plot, c4_html, c4_plot])
+    demo.load(refresh_dashboard, outputs=[c1_h, c1_p, c2_h, c2_p, c3_h, c3_p, c4_h, c4_p])
+    btn.click(refresh_dashboard, outputs=[c1_h, c1_p, c2_h, c2_p, c3_h, c3_p, c4_h, c4_p])
 
 if __name__ == "__main__":
     demo.launch()