Update app.py

app.py

@@ -8,7 +8,6 @@ import spaces
 # --- Initialize the Model Pipeline (No changes) ---
 print("Loading MedGemma model...")
 try:
-    # Using "image-to-text" is more robust for modern multimodal chat models.
     pipe = pipeline(
         "image-to-text",
         model="google/medgemma-4b-it",
@@ -26,8 +25,8 @@ except Exception as e:
 @spaces.GPU()
 def analyze_symptoms(symptom_image: Image.Image, symptoms_text: str):
     """
-    Analyzes user's symptoms using the officially recommended chat format
-    for the MedGemma multimodal model.
+    Analyzes user's symptoms using the recommended chat format and correct
+    parameter passing for the MedGemma multimodal model.
     """
     if not model_loaded:
         return "Error: The AI model could not be loaded. Please check the Space logs."
@@ -37,9 +36,7 @@ def analyze_symptoms(symptom_image: Image.Image, symptoms_text: str):
         return "Please describe your symptoms or upload an image for analysis."
 
     try:
-        # --- DEFINITIVE CHAT-BASED PROMPT LOGIC ---
-
-        # 1. System Prompt: This sets the AI's persona and overall goal.
+        # --- CHAT-BASED PROMPT LOGIC (Unchanged) ---
         system_instruction = (
             "You are an expert, empathetic AI medical assistant. "
             "Analyze the potential medical condition based on the user's input. "
@@ -48,50 +45,41 @@ def analyze_symptoms(symptom_image: Image.Image, symptoms_text: str):
             "the user provided."
         )
 
-        # 2. User Content: This must be a list of dictionaries for multimodal input.
         user_content = []
-
-        # The model requires some form of text. If the user provides none,
-        # we add a generic prompt to accompany the image.
         text_to_send = symptoms_text if symptoms_text else "Please analyze this medical image."
         user_content.append({"type": "text", "text": text_to_send})
 
-        # Add the image part if it exists.
         if symptom_image:
             user_content.append({"type": "image", "image": symptom_image})
 
-        # 3. Construct the full message list for the pipeline
         messages = [
             {"role": "system", "content": system_instruction},
             {"role": "user", "content": user_content},
         ]
-
-        print("Generating pipeline output with chat format...")
         
-        # --- CORRECTED PIPELINE CALL ---
-        # Pass the `messages` list directly. The pipeline's processor, which knows
-        # the model's chat template, will format it correctly.
-        output = pipe(
-            messages,
-            max_new_tokens=512,
-            do_sample=True,
-            temperature=0.7
-        )
+        print("Generating pipeline output with chat format...")
+
+        # --- DEFINITIVE PIPELINE CALL ---
+        # All text-generation parameters must be nested within a 'generate_kwargs' dictionary.
+        generate_kwargs = {
+            "max_new_tokens": 512,
+            "do_sample": True,
+            "temperature": 0.7,
+        }
+
+        # The `messages` list is the primary argument.
+        # `generate_kwargs` is a dedicated keyword argument for generation options.
+        output = pipe(messages, generate_kwargs=generate_kwargs)
 
         print("Pipeline Output:", output)
 
-        # --- ROBUST OUTPUT PROCESSING ---
-        # The output from a chat-templated pipeline call is a list containing the full
-        # conversation history, including the newly generated assistant message.
+        # --- OUTPUT PROCESSING (Unchanged) ---
         if output and isinstance(output, list) and output[0].get('generated_text'):
-            # The generated_text contains the full conversation history
             full_conversation = output[0]['generated_text']
-            # The last message in the list is the AI's response.
             assistant_message = full_conversation[-1]
             if assistant_message['role'] == 'assistant':
                 result = assistant_message['content']
             else:
-                # Fallback in case the last message isn't from the assistant
                 result = str(assistant_message)
         else:
             result = "The model did not return a valid response. Please try again."
@@ -104,8 +92,7 @@ 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)}"
 
-
-# --- Create the Gradio Interface (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;">
@@ -144,4 +131,4 @@ with gr.Blocks(theme=gr.themes.Soft(), title="AI Symptom Analyzer") as demo:
 
 if __name__ == "__main__":
     print("Starting Gradio interface...")
-    demo.launch(debug=True)
+    demo.launch(debug=True)