Update app.py

app.py

@@ -1,19 +1,38 @@
-from flask import Flask, request, jsonify
-from model import load_model, predict
+import gradio as gr
+from mock_model import predict
 
-app = Flask(__name__)
+def run_app(amount, stage, industry, lead_score, emails, meetings, close_gap):
+    input_data = {
+        "amount": amount,
+        "stage": stage,
+        "industry": industry,
+        "lead_score": lead_score,
+        "emails_last_7_days": emails,
+        "meetings_last_30_days": meetings,
+        "close_date_gap": close_gap
+    }
+    result = predict(input_data, None, None, None)
+    return result["score"], result["confidence"], result["risk"], result["recommendation"]
 
-# Load mock model components
-model, tokenizer, summarizer = load_model()
-
-@app.route("/score", methods=["POST"])
-def score():
-    try:
-        data = request.get_json()
-        result = predict(data, model, tokenizer, summarizer)
-        return jsonify(result), 200
-    except Exception as e:
-        return jsonify({"error": str(e)}), 500
+demo = gr.Interface(
+    fn=run_app,
+    title="AI-Powered Deal Qualification Engine (Demo)",
+    inputs=[
+        gr.Number(label="Amount (USD)", value=50000),
+        gr.Dropdown(["Prospecting", "Proposal/Price Quote", "Negotiation", "Closed Won", "Closed Lost"], label="Stage"),
+        gr.Textbox(label="Industry", value="Software"),
+        gr.Number(label="Lead Score", value=85),
+        gr.Number(label="Emails in Last 7 Days", value=3),
+        gr.Number(label="Meetings in Last 30 Days", value=2),
+        gr.Number(label="Close Date Gap (days)", value=10)
+    ],
+    outputs=[
+        gr.Number(label="AI Score (0–100)"),
+        gr.Number(label="Confidence (0–1)"),
+        gr.Textbox(label="Risk Level"),
+        gr.Textbox(label="Recommendation")
+    ]
+)
 
 if __name__ == "__main__":
-    app.run(host="0.0.0.0", port=7860)
+    demo.launch()