Update app.py

app.py

@@ -1,35 +1,62 @@
 import gradio as gr
-import asyncio
-from AICoreAGIX_with_TB import AICoreAGIX
+from huggingface_hub import InferenceClient
+from Codriaoagix import AICoreAGIX  # Ensure this imports your AICoreAGIX class
 
+# Initialize the AI core
 ai_core = AICoreAGIX()
 
-async def diagnose_tb(image_file, audio_file):
-    user_id = 1  # Example user
-    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']}"
-    )
-
-# Async wrapper for Gradio
-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)")
+def respond(message, history, system_message, max_tokens, temperature, top_p, image, audio):
+    # Process the uploaded files
+    if image and audio:
+        # Save the uploaded files to disk or process them as needed
+        image_path = "uploaded_image.png"
+        audio_path = "uploaded_audio.wav"
+        image.save(image_path)
+        audio.save(audio_path)
+
+        # Run TB diagnostics
+        tb_result = ai_core.run_tb_diagnostics(image_path, audio_path, user_id=1)  # Replace with actual user_id handling
+
+        # Incorporate TB diagnostic results into the response
+        tb_message = f"TB Diagnostic Result: {tb_result['tb_risk']}\n"
+        tb_message += f"Image Analysis: {tb_result['image_analysis']}\n"
+        tb_message += f"Audio Analysis: {tb_result['audio_analysis']}\n"
+        tb_message += f"Shareable Link: {tb_result['shareable_link']}\n\n"
+    else:
+        tb_message = "No TB diagnostic data provided.\n\n"
+
+    # Existing chat functionality
+    messages = [{"role": "system", "content": system_message}]
+    for user_msg, bot_msg in history:
+        if user_msg:
+            messages.append({"role": "user", "content": user_msg})
+        if bot_msg:
+            messages.append({"role": "assistant", "content": bot_msg})
+    messages.append({"role": "user", "content": message})
+
+    response = ""
+    for message in client.chat_completion(
+        messages,
+        max_tokens=max_tokens,
+        stream=True,
+        temperature=temperature,
+        top_p=top_p,
+    ):
+        token = message.choices[0].delta.content
+        response += token
+        yield tb_message + response
+
+# Define the Gradio interface
+demo = gr.ChatInterface(
+    respond,
+    additional_inputs=[
+        gr.Textbox(value="You are a friendly Chatbot.", 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)"),
+        gr.inputs.Image(type="pil", label="Upload Saliva Microscopy Image"),
+        gr.inputs.Audio(type="file", label="Upload Cough Audio Recording"),
     ],
-    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__":