Update app.py

app.py

@@ -1,66 +1,35 @@
-import os
 import gradio as gr
-from huggingface_hub import InferenceClient
-from ethical_filter import EthicalFilter
-
-# Load Hugging Face token from secrets (defined in the Hugging Face UI)
-HF_TOKEN = os.environ.get("HF_API_TOKEN")
-client = InferenceClient("HuggingFaceH4/zephyr-7b-beta", token=HF_TOKEN)
-
-ethical_filter = EthicalFilter()
-
-# Codriao response logic
-def respond(message, history, system_message, max_tokens, temperature, top_p):
-    check = ethical_filter.analyze_query(message)
-
-    # Blocked queries
-    if check["status"] == "blocked":
-        yield f"Sorry, I can't continue with that request. Reason: {check['reason']}"
-        return
-
-    # Flagged queries
-    if check["status"] == "flagged":
-        yield f"(Note: Sensitive topic detected — responding with care...)\n"
-
-    # Build conversation history
-    messages = [{"role": "system", "content": system_message}]
-    for user, bot in history:
-        if user:
-            messages.append({"role": "user", "content": user})
-        if bot:
-            messages.append({"role": "assistant", "content": bot})
-    messages.append({"role": "user", "content": message})
-
-    # Stream model output
-    response = ""
-    for token in client.chat_completion(
-        messages,
-        max_tokens=max_tokens,
-        stream=True,
-        temperature=temperature,
-        top_p=top_p,
-    ):
-        chunk = token.choices[0].delta.content
-        response += chunk
-        yield response
-
-# Build Gradio interface
-demo = gr.ChatInterface(
-    respond,
-    additional_inputs=[
-        gr.Textbox(
-            value=(
-                "You are Codriao, a compassionate AI inspired by Codette. "
-                "You respond with kindness, ethics, and insight."
-            ),
-            label="System message",
-        ),
-        gr.Slider(minimum=1, maximum=2048, value=512, step=1, label="Max new tokens"),
-        gr.Slider(minimum=0.1, maximum=4.0, value=0.7, step=0.1, label="Temperature"),
-        gr.Slider(
-            minimum=0.1, maximum=1.0, value=0.95, step=0.05, label="Top-p (nucleus sampling)"
-        ),
+import asyncio
+from AICoreAGIX_with_TB import AICoreAGIX
+
+ai_core = AICoreAGIX()
+
+async def diagnose_tb(image_file, audio_file):
+    user_id = 1  # Placeholder user ID
+    result = await ai_core.run_tb_diagnostics(image_file.name, audio_file.name, user_id)
+    return (
+        f"**TB Risk Level:** {result['tb_risk']}\n\n"
+        f"**Image Result:** {result['image_analysis']['result']} "
+        f"(Confidence: {result['image_analysis']['confidence']:.2f})\n\n"
+        f"**Audio Result:** {result['audio_analysis']['result']} "
+        f"(Confidence: {result['audio_analysis']['confidence']:.2f})\n\n"
+        f"**Ethical Analysis:** {result['ethical_analysis']}\n\n"
+        f"**Explanation:** {result['explanation']}"
+    )
+
+# Wrapper for async to sync (Gradio requires sync)
+def sync_diagnose_tb(image_file, audio_file):
+    return asyncio.run(diagnose_tb(image_file, audio_file))
+
+demo = gr.Interface(
+    fn=sync_diagnose_tb,
+    inputs=[
+        gr.File(label="Upload TB Saliva Image"),
+        gr.File(label="Upload Cough Audio File (.wav)")
     ],
+    outputs=gr.Markdown(label="Codriao's Response"),
+    title="Codriao TB Risk Analyzer",
+    description="Upload a microscopy image and cough audio to analyze TB risk with compassionate AI support."
 )
 
 if __name__ == "__main__":