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import gradio as gr
from PIL import Image
from ultralytics import YOLO
from transformers import pipeline
import torch
# ——— 1) Chart-pattern detector (YOLOv8) ———
pattern_model = YOLO("model.pt")
# ——— 2) Trading-Hero-LLM pipeline ———
qa_pipeline = pipeline(
"text-generation",
model="fuchenru/Trading-Hero-LLM",
max_new_tokens=100,
device=0 if torch.cuda.is_available() else -1
)
def analyze_charts(img1: Image.Image, img2: Image.Image):
output = []
# Chart 1
if img1:
res1 = pattern_model(img1)[0]
labels1 = [res1.names[int(b.cls[0])] for b in res1.boxes]
output.append(
"📊 Chart 1 Patterns:\n" +
("\n".join(f"• {lbl}" for lbl in labels1) if labels1 else "No patterns detected.")
)
else:
output.append("🖼️ Chart 1: No image uploaded.")
# Chart 2
if img2:
res2 = pattern_model(img2)[0]
labels2 = [res2.names[int(b.cls[0])] for b in res2.boxes]
output.append(
"\n📊 Chart 2 Patterns:\n" +
("\n".join(f"• {lbl}" for lbl in labels2) if labels2 else "No patterns detected.")
)
else:
output.append("\n🖼️ Chart 2: No image uploaded.")
return "\n".join(output)
def answer_question(question: str):
if not question.strip():
return "❌ Please enter a question."
resp = qa_pipeline(question)[0]["generated_text"]
return resp
# ——— Gradio UI ———
with gr.Blocks() as demo:
gr.Markdown("## 📈 Nifty AI Trading Assistant")
with gr.Row():
img1 = gr.Image(type="pil", label="Upload Chart 1")
img2 = gr.Image(type="pil", label="Upload Chart 2")
analyze_btn = gr.Button("🔍 Analyze Charts")
pattern_out = gr.Textbox(label="Chart Pattern Output")
analyze_btn.click(fn=analyze_charts, inputs=[img1, img2], outputs=pattern_out)
gr.Markdown("---")
question = gr.Textbox(label="💬 Ask a Trading Question")
answer_btn = gr.Button("🤖 Get LLM Response")
llm_out = gr.Textbox(label="LLM Answer")
answer_btn.click(fn=answer_question, inputs=question, outputs=llm_out)
demo.launch()
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