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| import gradio as gr | |
| from transformers import pipeline | |
| from PIL import Image | |
| import torch | |
| import os | |
| import spaces | |
| # --- Initialize the Model Pipeline --- | |
| print("Loading MedGemma model...") | |
| try: | |
| pipe = pipeline( | |
| "image-text-to-text", | |
| model="google/medgemma-4b-it", | |
| torch_dtype=torch.bfloat16, | |
| device_map="auto", | |
| token=os.environ.get("HF_TOKEN") | |
| ) | |
| model_loaded = True | |
| print("Model loaded successfully!") | |
| except Exception as e: | |
| model_loaded = False | |
| print(f"Error loading model: {e}") | |
| # --- Core Analysis Function --- | |
| def analyze_symptoms(symptom_image, symptoms_text): | |
| """ | |
| Analyzes user's symptoms using a corrected prompt-building logic. | |
| """ | |
| if not model_loaded: | |
| return "Error: The AI model could not be loaded. Please check the Space logs." | |
| # Standardize input to avoid issues with None or whitespace | |
| symptoms_text = symptoms_text.strip() if symptoms_text else "" | |
| if symptom_image is None and not symptoms_text: | |
| return "Please describe your symptoms or upload an image for analysis." | |
| try: | |
| # --- REVISED PROMPT LOGIC --- | |
| # Build the prompt dynamically based on provided inputs. | |
| # This is much clearer and less error-prone. | |
| prompt_parts = [ | |
| "You are an expert, empathetic AI medical assistant. Analyze the potential medical condition based on the following information.", | |
| "Provide a list of possible conditions, your reasoning, and a clear, actionable next-steps plan.", | |
| "Start your analysis by describing the user-provided information (text and/or image)." | |
| ] | |
| # This is the actual user input that the model will process. | |
| # It's better to pass it directly instead of wrapping it in another instruction. | |
| user_input_for_model = [] | |
| if symptoms_text: | |
| user_input_for_model.append({"type": "text", "text": symptoms_text}) | |
| if symptom_image: | |
| # The pipeline expects an image object. PIL Image is correct. | |
| user_input_for_model.append({"type": "image", "image": symptom_image}) | |
| # The system prompt sets the context and instructions for the AI. | |
| system_prompt = " ".join(prompt_parts) | |
| messages = [ | |
| { | |
| "role": "system", | |
| "content": [{"type": "text", "text": system_prompt}] | |
| }, | |
| { | |
| "role": "user", | |
| "content": user_input_for_model | |
| } | |
| ] | |
| print("Generating pipeline output...") | |
| output = pipe(messages, max_new_tokens=512, do_sample=True, temperature=0.7) | |
| # The output format is a list containing the full conversation history. | |
| # The last message in the list is the AI's response. | |
| print("Pipeline Output:", output) | |
| # Make the output processing more robust | |
| generated = output[0]["generated_text"] | |
| if isinstance(generated, list) and generated: | |
| # If output is a list of dicts, take the content from the last one | |
| result = generated[-1].get('content', str(generated)) | |
| elif isinstance(generated, str): | |
| # If output is just a string | |
| result = generated | |
| else: | |
| # Failsafe for any other unexpected format | |
| result = str(generated) | |
| 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.***" | |
| return result + disclaimer | |
| except Exception as e: | |
| print(f"An exception occurred during analysis: {type(e).__name__}: {e}") | |
| return f"Error during analysis: {str(e)}" | |
| # --- Create the Gradio Interface (No changes needed here) --- | |
| 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;"> | |
| <h1>🩺 AI Symptom Analyzer</h1> | |
| <p>Advanced symptom analysis powered by Google's MedGemma AI</p> | |
| </div> | |
| """) | |
| gr.HTML(""" | |
| <div style="background-color: #fff3cd; border: 1px solid #ffeaa7; border-radius: 8px; padding: 1rem; margin: 1rem 0; color: #856404;"> | |
| <strong>⚠️ Medical Disclaimer:</strong> This AI tool is for informational purposes only and is not a substitute for professional medical diagnosis or treatment. | |
| </div> | |
| """) | |
| with gr.Row(equal_height=True): | |
| with gr.Column(scale=1): | |
| gr.Markdown("### 1. Describe Your Symptoms") | |
| symptoms_input = gr.Textbox( | |
| label="Symptoms", | |
| placeholder="e.g., 'I have a rash on my arm that is red and itchy...'", lines=5) | |
| gr.Markdown("### 2. Upload an Image (Optional)") | |
| image_input = gr.Image(label="Symptom Image", type="pil", height=300) | |
| with gr.Row(): | |
| clear_btn = gr.Button("🗑️ Clear All", variant="secondary") | |
| analyze_btn = gr.Button("🔍 Analyze Symptoms", variant="primary", size="lg") | |
| with gr.Column(scale=1): | |
| gr.Markdown("### 📊 Analysis Report") | |
| output_text = gr.Textbox( | |
| label="AI Analysis", lines=25, show_copy_button=True, placeholder="Analysis results will appear here...") | |
| def clear_all(): | |
| return None, "", "" | |
| analyze_btn.click(fn=analyze_symptoms, inputs=[image_input, symptoms_input], outputs=output_text) | |
| clear_btn.click(fn=clear_all, outputs=[image_input, symptoms_input, output_text]) | |
| if __name__ == "__main__": | |
| print("Starting Gradio interface...") | |
| demo.launch(debug=True) |