Update app.py

app.py

@@ -1,7 +1,5 @@
 import os
 import subprocess
-from typing import List, Dict, Any
-
 import gradio as gr
 import chromadb
 
@@ -15,6 +13,8 @@ INDEX = None
 
 
 def get_persist_dir():
+    # On free Spaces, /data usually won't exist.
+    # If you prebuilt locally and uploaded storage/chroma, this will use that.
     return "/data/chroma" if os.path.exists("/data") else "storage/chroma"
 
 
@@ -91,36 +91,17 @@ def format_sources(response, max_sources=3):
     return output
 
 
-def respond(
-    message: str,
-    history: List[Dict[str, Any]],
-    model_name: str,
-    temperature: float,
-    top_k: int,
-    show_sources: bool,
-):
-    if history is None:
-        history = []
-
-    if not message or not message.strip():
-        return history, ""
-
+def chat_fn(message, history):
     if not os.getenv("OPENAI_API_KEY"):
-        history = history + [{
-            "role": "assistant",
-            "content": "OPENAI_API_KEY missing. Add it in Hugging Face Space secrets."
-        }]
-        return history, ""
-
-    history = history + [{"role": "user", "content": message.strip()}]
+        return "OPENAI_API_KEY missing. Add it in Hugging Face Space secrets."
 
     try:
         index = get_index()
-        llm = OpenAI(model=model_name, temperature=float(temperature))
+        llm = OpenAI(model="gpt-4o-mini", temperature=0.2)
 
         query_engine = index.as_query_engine(
             llm=llm,
-            similarity_top_k=int(top_k),
+            similarity_top_k=3,
             response_mode="compact"
         )
 
@@ -135,53 +116,28 @@ def respond(
         response = query_engine.query(prompt)
         answer = str(response)
 
-        if show_sources:
-            answer += format_sources(response, max_sources=min(int(top_k), 3))
+        # Optional: enable this if you want evidence snippets always appended
+        # answer += format_sources(response, max_sources=3)
 
-    except Exception as e:
-        answer = f"Error: {str(e)}"
-
-    history = history + [{"role": "assistant", "content": answer}]
-    return history, ""
+        return answer
 
-
-def clear_chat():
-    return []
+    except Exception as e:
+        return f"Error: {str(e)}"
 
 
 with gr.Blocks() as demo:
-    gr.Markdown("# 🧠 Neurology Tutor")
+    gr.Markdown("# 🧠 BrainChat")
     gr.Markdown("Automatic pipeline: PDF extraction → chapter text → vector DB → chatbot")
 
-    chatbot = gr.Chatbot(height=500, type="messages")
-    msg = gr.Textbox(placeholder="Ask a question...", lines=1)
-
-    with gr.Row():
-        model_name = gr.Dropdown(
-            ["gpt-4o-mini", "gpt-4.1-mini"],
-            value="gpt-4o-mini",
-            label="Model"
+    gr.ChatInterface(
+        fn=chat_fn,
+        title="Neurology Tutor",
+        description="Ask questions from your uploaded neurology PDFs.",
+        textbox=gr.Textbox(
+            placeholder="Ask a question...",
+            lines=1
         )
-        temperature = gr.Slider(0.0, 0.8, value=0.2, step=0.1, label="Temperature")
-
-    with gr.Row():
-        top_k = gr.Slider(1, 5, value=3, step=1, label="Top-K Chunks")
-        show_sources = gr.Checkbox(value=False, label="Show Sources")
-
-    clear_btn = gr.Button("Clear Chat")
-
-    msg.submit(
-        respond,
-        inputs=[msg, chatbot, model_name, temperature, top_k, show_sources],
-        outputs=[chatbot, msg]
     )
 
-    clear_btn.click(
-        clear_chat,
-        inputs=[],
-        outputs=[chatbot]
-    )
-
-
 if __name__ == "__main__":
     demo.launch()