Upload app.py

app.py

@@ -6,7 +6,6 @@ Using distilgpt2 for Ultra-Fast CPU Inference (Debug Mode)
 import os
 import torch
 from transformers import AutoTokenizer, AutoModelForCausalLM
-import yfinance as yf
 import gradio as gr
 import warnings
 warnings.filterwarnings('ignore')
@@ -35,7 +34,6 @@ try:
     tokenizer = AutoTokenizer.from_pretrained(model_name)
     model = AutoModelForCausalLM.from_pretrained(model_name)
     
-    # Move to GPU if available, else CPU
     if torch.cuda.is_available():
         model = model.cuda()
         print("✓ Model on GPU")
@@ -47,52 +45,22 @@ except Exception as e:
     print(f"⚠ Model loading error: {e}")
     raise
 
-# ===============================
-# API LOOKUPS + CONTEXT ENRICHMENT
-# ===============================
-print("\n[2] Loading Market Data Engine...")
-
-def fetch_market_context(query):
-    """
-    Fetch live market data and enrich prompt with real numbers.
-    """
-    context = ""
-    tickers = ["AAPL", "MSFT", "NVDA", "TSLA", "GOOGL", "AMZN", "BTC-USD", "ETH-USD"]
-    
-    for ticker in tickers:
-        if ticker.lower() in query.lower():
-            try:
-                data = yf.Ticker(ticker).info
-                price = data.get('currentPrice', 'N/A')
-                pe = data.get('trailingPE', 'N/A')
-                market_cap = data.get('marketCap', 'N/A')
-                context += f"\n{ticker}: Price=${price}, P/E={pe}, Market Cap=${market_cap}"
-            except:
-                context += f"\n{ticker}: (live data unavailable)"
-    
-    return context if context else "\n(No specific tickers mentioned; generic market context mode)"
-
 # ===============================
 # INFERENCE ENGINE
 # ===============================
-print("\n[3] Loading Inference Engine...")
+print("\n[2] Loading Inference Engine...")
 
-def sisko_query(user_query, max_tokens=30):
+def sisko_query(user_query, max_tokens=20):
     """
-    Generate response using distilgpt2 + market context.
+    Generate response using distilgpt2.
     """
     try:
-        market_context = fetch_market_context(user_query)
-        
-        # Build simple prompt for distilgpt2
         prompt = f"Question: {user_query}\nAnswer:"
         
-        # Tokenize and prepare
         inputs = tokenizer(prompt, return_tensors="pt")
         device = next(model.parameters()).device
         inputs = {k: v.to(device) for k, v in inputs.items()}
         
-        # Generate response
         with torch.no_grad():
             outputs = model.generate(
                 **inputs,
@@ -102,51 +70,45 @@ def sisko_query(user_query, max_tokens=30):
                 do_sample=True
             )
         
-        # Decode and clean
         full_resp = tokenizer.decode(outputs[0], skip_special_tokens=True)
-        
-        return full_resp.strip() if full_resp else "Unable to generate response at this time. Please try again."
+        return full_resp.strip() if full_resp else "Unable to generate response."
     
     except Exception as e:
         return f"Error: {str(e)[:80]}"
 
-print("✓ Inference Engine Ready (distilgpt2 + Live Market Data)")
+print("✓ Inference Engine Ready (distilgpt2)")
 
 # ===============================
 # GRADIO UI
 # ===============================
-print("\n[4] Launching Gradio UI...")
-
-def chat_sisko(message, history):
-    try:
-        resp = sisko_query(message)
-    except Exception as e:
-        resp = f"Error: {str(e)[:80]}"
-    history.append((message, resp))
-    return history, history
+print("\n[3] Launching Gradio UI...")
 
 with gr.Blocks(theme=gr.themes.Soft(primary_hue="blue")) as demo:
     gr.Markdown(
         """
         # 🤖 Sisko AI: FinKing
         ### AI-Powered Investing for Superior Returns
-        **Powered by distilgpt2 + Live Market Data (Debug Mode)**
+        **Powered by distilgpt2 (Debug Mode)**
         
         Annual Return: **27%** | Sharpe Ratio: **0.82** | Volatility: **12%**
-        
-        *Strategy: Small LLM + API Lookups + Prompt Context for fast, accurate responses*
         """
     )
-    gr.ChatInterface(
-        fn=chat_sisko,
-        title="Sisko AI: FinKing Chat",
-        description="Stock, Crypto, Portfolio & Macro Analytics",
-        examples=[
-            "What is 2+2?",
-            "Tell me about AAPL",
-            "Bitcoin outlook?"
-        ]
+    
+    with gr.Row():
+        prompt_input = gr.Textbox(label="Ask me anything", placeholder="What is 2+2?")
+        output = gr.Textbox(label="Response")
+    
+    submit_btn = gr.Button("Submit")
+    submit_btn.click(fn=sisko_query, inputs=prompt_input, outputs=output)
+    
+    gr.Examples(
+        examples=["What is 2+2?", "Tell me about AAPL", "Bitcoin outlook?"],
+        inputs=prompt_input,
+        outputs=output,
+        fn=sisko_query,
+        cache_examples=False
     )
+    
     gr.Markdown(
         """
         ---