Update app.py

app.py

@@ -1,63 +1,74 @@
 import gradio as gr
-from huggingface_hub import InferenceClient
-from AICoreAGIX_with_TB import AICoreAGIX# Ensure this imports your AICoreAGIX class
+import os
+from HuggingFaceHelper import HuggingFaceHelper
+from AICoreAGIX_with_TB import AICoreAGIX
 
-# Initialize the AI core
+# Initialize AI Core for TB analysis
 ai_core = AICoreAGIX()
 
-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"),
+# Initialize Hugging Face training helper
+helper = HuggingFaceHelper(model_path="./merged_model")
+
+def diagnose_tb(image_file, audio_file):
+    user_id = 1  # Placeholder user ID
+    result = 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']}\n\n"
+        f"**Shareable Link:** {result['shareable_link']}"
+    )
+
+def upload_and_finetune(jsonl_file):
+    save_path = f"./training_data/{jsonl_file.name}"
+    os.makedirs("training_data", exist_ok=True)
+
+    with open(save_path, "wb") as f:
+        f.write(jsonl_file.read())
+
+    # Trigger fine-tuning
+    helper.dataset_path = save_path
+    helper.fine_tune(output_dir="./codette_finetuned")
+
+    return f"✅ Fine-tuning complete! Model updated and stored."
+
+def get_latest_model():
+    return "Download the latest fine-tuned Codriao model here: https://huggingface.co/Raiff1982/codriao-finetuned"
+
+# Gradio UI
+demo = gr.TabbedInterface(
+    [
+        gr.Interface(
+            fn=diagnose_tb,
+            inputs=[
+                gr.File(label="Upload TB Saliva Image"),
+                gr.File(label="Upload Cough Audio File (.wav)")
+            ],
+            outputs="text",
+            title="Codriao TB Risk Analyzer",
+            description="Upload a microscopy image and cough audio to analyze TB risk with compassionate AI support."
+        ),
+        gr.Interface(
+            fn=upload_and_finetune,
+            inputs=[gr.File(label="Upload JSONL Training Data")],
+            outputs="text",
+            title="Codriao Fine-Tuning Trainer",
+            description="Upload JSONL files to teach Codriao new knowledge."
+        ),
+        gr.Interface(
+            fn=get_latest_model,
+            inputs=[],
+            outputs="text",
+            title="Download Codriao's Fine-Tuned Model"
+        )
     ],
+    title="Codriao AI System",
+    description="Train Codriao, run TB diagnostics, and download updated models."
 )
 
 if __name__ == "__main__":
-    demo.launch()
+    demo.launch()