Upload app.py

app.py

@@ -1,14 +1,38 @@
-from huggingface_hub import InferenceClient
-import os
-
-client = InferenceClient(token=os.environ.get("HF_API_TOKEN"))
-
-try:
-    response = client.text_generation(
-        model="tiiuae/falcon-7b-instruct",
-        prompt="Hello, who are you?",
-        max_new_tokens=50
-    )
-    print(response)
-except Exception as e:
-    print(f"Test failed: {e}")
+import gradio as gr
+from agents import build_graph
+import asyncio
+
+# Build the LangGraph once
+graph = build_graph()
+
+async def run_book_recommender(user_input):
+    initial_state = {"user_input": user_input}
+
+    async for state in graph.astream(initial_state):
+        final_state = state
+
+    print("[app.py] Final state:", final_state)
+
+    # Access the nested "reasoning" key
+    reasoning_data = final_state.get("reasoning", {})
+    recommendations = reasoning_data.get("final_recommendations", [])
+    reasoning = reasoning_data.get("final_reasoning", "")
+
+    recommendations_text = "\n\n".join(
+        [f"📘 {rec['title']}\n🔗 {rec.get('link', '')}\n💡 {rec.get('reason', '')}" for rec in recommendations]
+    ) or "No recommendations found."
+
+    return recommendations_text, reasoning
+
+# Gradio UI
+with gr.Blocks() as demo:
+    gr.Markdown("# 📚 AI Book Recommender")
+    user_input = gr.Textbox(label="Tell me some books you like")
+    recommend_btn = gr.Button("Get Recommendations")
+    recommendations_output = gr.Textbox(label="Recommended Books", lines=10)
+    reasoning_output = gr.Textbox(label="Reasoning / Debug Log", lines=15)
+
+    recommend_btn.click(run_book_recommender, inputs=user_input, outputs=[recommendations_output, reasoning_output])
+
+if __name__ == "__main__":
+    demo.launch()