Update app.py

app.py

@@ -5,7 +5,7 @@ import torch
 import os
 import spaces
 
-# --- Initialize the Model Pipeline (As per your working example) ---
+# --- Initialize the Model Pipeline ---
 print("Loading MedGemma model...")
 try:
     pipe = pipeline(
@@ -21,12 +21,11 @@ except Exception as e:
     model_loaded = False
     print(f"Error loading model: {e}")
 
-# --- Core Analysis Function (Using the logic from your working example) ---
+# --- Core Analysis Function ---
 @spaces.GPU()
 def analyze_symptoms(symptom_image: Image.Image, symptoms_text: str):
     """
-    Analyzes user's symptoms using the definitive calling convention demonstrated
-    in the working X-ray analyzer example.
+    Analyzes user's symptoms using the definitive calling convention.
     """
     if not model_loaded:
         return "Error: The AI model could not be loaded. Please check the Space logs."
@@ -36,7 +35,6 @@ def analyze_symptoms(symptom_image: Image.Image, symptoms_text: str):
         return "Please describe your symptoms or upload an image for analysis."
 
     try:
-        # --- DEFINITIVE MESSAGE CONSTRUCTION (from your example) ---
         system_prompt = (
             "You are an expert, empathetic AI medical assistant. Analyze the potential "
             "medical condition based on the following information. Provide a list of "
@@ -45,10 +43,8 @@ def analyze_symptoms(symptom_image: Image.Image, symptoms_text: str):
         )
 
         user_content = []
-        # The user's prompt text is always present.
         user_content.append({"type": "text", "text": symptoms_text})
         
-        # The actual PIL image object is added to the content list if it exists.
         if symptom_image:
             user_content.append({"type": "image", "image": symptom_image})
             
@@ -57,17 +53,17 @@ def analyze_symptoms(symptom_image: Image.Image, symptoms_text: str):
             {"role": "user", "content": user_content}
         ]
         
+        # *** THE FIX: Increased the token limit to prevent truncated output ***
         generation_args = {
-            "max_new_tokens": 512,
+            "max_new_tokens": 1024,  # Increased from 512 to 1024
             "do_sample": True,
             "temperature": 0.7,
         }
         
-        # --- DEFINITIVE PIPELINE CALL (from your example) ---
         # The entire messages structure is passed to the `text` argument.
         output = pipe(text=messages, **generation_args)
         
-        # The result is the 'content' of the last generated message.
+        # Extract the content of the last generated message.
         result = output[0]["generated_text"][-1]["content"]
 
         disclaimer = "\n\n***Disclaimer: I am an AI assistant and not a medical professional. This is not a diagnosis. Please consult a doctor for any health concerns.***"
@@ -78,7 +74,7 @@ def analyze_symptoms(symptom_image: Image.Image, symptoms_text: str):
         print(f"An exception occurred during analysis: {type(e).__name__}: {e}")
         return f"An error occurred during analysis. Please check the logs for details: {str(e)}"
 
-# --- Gradio Interface (Your original, no changes needed) ---
+# --- Gradio Interface (No changes needed) ---
 with gr.Blocks(theme=gr.themes.Soft(), title="AI Symptom Analyzer") as demo:
     gr.HTML("""
         <div style="text-align: center; background: linear-gradient(135deg, #667eea 0%, #764ba2 100%); color: white; padding: 2rem; border-radius: 10px; margin-bottom: 2rem;">
@@ -109,11 +105,7 @@ with gr.Blocks(theme=gr.themes.Soft(), title="AI Symptom Analyzer") as demo:
             output_text = gr.Textbox(
                 label="AI Analysis", lines=25, show_copy_button=True, placeholder="Analysis results will appear here...")
 
-    def clear_all():
-        # This function should return values for all outputs cleared by the button
-        return None, ""
-
-    # The clear button now correctly clears the image and text input.
+    # Event handlers
     analyze_btn.click(fn=analyze_symptoms, inputs=[image_input, symptoms_input], outputs=output_text)
     clear_btn.click(fn=lambda: (None, "", ""), outputs=[image_input, symptoms_input, output_text])