Create app.py

app.py

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+import gradio as gr
+from PIL import Image
+from ultralytics import YOLO
+from transformers import AutoTokenizer, AutoModelForCausalLM
+import torch
+
+# Load YOLOv8 pattern detector
+pattern_model = YOLO("foduucom/stockmarket-pattern-detection-yolov8")
+
+# Load Trading-Hero-LLM
+llm_tokenizer = AutoTokenizer.from_pretrained("fuchenru/Trading-Hero-LLM")
+llm_model = AutoModelForCausalLM.from_pretrained("fuchenru/Trading-Hero-LLM")
+
+# Function: Run both models
+def analyze_chart_and_question(image, question):
+    results = []
+
+    # Pattern detection with YOLO
+    if image:
+        boxes = pattern_model(image)[0]
+        names = boxes.names
+        results.append("📊 Detected Chart Patterns:")
+        for box in boxes.boxes:
+            cls = int(box.cls[0])
+            label = names[cls]
+            results.append(f"• {label}")
+    else:
+        results.append("🖼️ No image uploaded.")
+
+    # Trading-Hero-LLM response
+    if question:
+        inputs = llm_tokenizer(question, return_tensors="pt")
+        with torch.no_grad():
+            output = llm_model.generate(**inputs, max_new_tokens=100)
+        response = llm_tokenizer.decode(output[0], skip_special_tokens=True)
+        results.append("\n🧠 Trading Hero LLM Response:\n" + response)
+    else:
+        results.append("💬 No question entered.")
+
+    return "\n".join(results)
+
+# Gradio UI
+iface = gr.Interface(
+    fn=analyze_chart_and_question,
+    inputs=[
+        gr.Image(label="Upload Nifty Chart", type="pil"),
+        gr.Textbox(label="Ask a Trading Question")
+    ],
+    outputs=gr.Textbox(label="AI Output"),
+    title="📊 Nifty Trading Assistant",
+    description="Upload a chart to detect patterns (YOLOv8) and ask a trading question (Trading-Hero-LLM)."
+)
+
+iface.launch()