init0

app.py

@@ -0,0 +1,275 @@
+import gradio as gr
+import torch
+from transformers import AutoTokenizer, AutoModelForCausalLM, BitsAndBytesConfig
+import re
+
+class MedGemmaSymptomAnalyzer:
+    def __init__(self):
+        self.model = None
+        self.tokenizer = None
+        self.model_loaded = False
+        print("Initializing MedGemma Symptom Analyzer...")
+    
+    def load_model(self):
+        """Load MedGemma model with optimizations for deployment"""
+        if self.model_loaded:
+            return True
+            
+        model_name = "google/medgemma-2b"
+        print(f"Loading model: {model_name}")
+        
+        try:
+            # First try without quantization for CPU compatibility
+            print("Loading tokenizer...")
+            self.tokenizer = AutoTokenizer.from_pretrained(model_name)
+            
+            print("Loading model...")
+            # Simplified loading for CPU/compatibility
+            self.model = AutoModelForCausalLM.from_pretrained(
+                model_name,
+                torch_dtype=torch.float32,  # Use float32 for CPU
+                device_map="cpu",  # Force CPU for compatibility
+                low_cpu_mem_usage=True
+            )
+            
+            if self.tokenizer.pad_token is None:
+                self.tokenizer.pad_token = self.tokenizer.eos_token
+                
+            self.model_loaded = True
+            print("Model loaded successfully!")
+            return True
+                
+        except Exception as e:
+            print(f"Error loading model: {e}")
+            print("Falling back to demo mode...")
+            self.model = None
+            self.tokenizer = None
+            self.model_loaded = False
+            return False
+    
+    def analyze_symptoms(self, symptoms_text, max_length=512, temperature=0.7):
+        """Analyze symptoms and provide medical insights"""
+        # Try to load model if not already loaded
+        if not self.model_loaded:
+            if not self.load_model():
+                # Fallback to demo response if model fails to load
+                return self._get_demo_response(symptoms_text)
+        
+        if not self.model or not self.tokenizer:
+            return self._get_demo_response(symptoms_text)
+        
+        # Format the prompt for medical analysis
+        prompt = f"""Patient presents with the following symptoms: {symptoms_text}
+
+Based on these symptoms, provide a medical analysis including:
+1. Possible differential diagnoses
+2. Recommended next steps
+3. When to seek immediate medical attention
+
+Medical Analysis:"""
+        
+        try:
+            # Tokenize input
+            inputs = self.tokenizer(
+                prompt,
+                return_tensors="pt",
+                truncation=True,
+                max_length=max_length,
+                padding=True
+            )
+            
+            # Generate response
+            with torch.no_grad():
+                outputs = self.model.generate(
+                    inputs.input_ids,
+                    attention_mask=inputs.attention_mask,
+                    max_new_tokens=400,
+                    temperature=temperature,
+                    do_sample=True,
+                    pad_token_id=self.tokenizer.eos_token_id,
+                    eos_token_id=self.tokenizer.eos_token_id
+                )
+            
+            # Decode response
+            response = self.tokenizer.decode(outputs[0], skip_special_tokens=True)
+            
+            # Extract only the generated part (after the prompt)
+            generated_text = response[len(prompt):].strip()
+            
+            return generated_text
+            
+        except Exception as e:
+            return f"Error during analysis: {str(e)}"
+    
+    def _get_demo_response(self, symptoms_text):
+        """Provide a demo response when model is not available"""
+        symptoms_lower = symptoms_text.lower()
+        
+        # Simple keyword-based demo responses
+        if any(word in symptoms_lower for word in ['fever', 'headache', 'fatigue', 'body aches']):
+            return """**DEMO MODE - Model not loaded**
+
+Based on the symptoms described (fever, headache, fatigue), here's a general analysis:
+
+**Possible Differential Diagnoses:**
+1. Viral infection (common cold, flu)
+2. Bacterial infection
+3. Stress or exhaustion
+4. Early signs of other conditions
+
+**Recommended Next Steps:**
+1. Rest and adequate hydration
+2. Monitor temperature regularly
+3. Over-the-counter pain relievers if appropriate
+4. Observe for worsening symptoms
+
+**When to Seek Immediate Medical Attention:**
+- High fever (>101.3°F/38.5°C)
+- Severe headache or neck stiffness
+- Difficulty breathing
+- Persistent vomiting
+- Symptoms worsen rapidly
+
+*Note: This is a demo response. For actual medical analysis, the MedGemma model needs to be loaded.*"""
+        
+        elif any(word in symptoms_lower for word in ['chest pain', 'breathing', 'shortness']):
+            return """**DEMO MODE - Model not loaded**
+
+Based on chest-related symptoms, here's a general analysis:
+
+**Possible Differential Diagnoses:**
+1. Respiratory infection
+2. Muscle strain
+3. Anxiety-related symptoms
+4. More serious conditions requiring evaluation
+
+**Recommended Next Steps:**
+1. Seek medical evaluation promptly
+2. Avoid strenuous activity
+3. Monitor breathing patterns
+4. Note any associated symptoms
+
+**When to Seek Immediate Medical Attention:**
+- Severe chest pain
+- Difficulty breathing
+- Pain spreading to arm, jaw, or back
+- Dizziness or fainting
+- These symptoms require immediate medical care
+
+*Note: This is a demo response. For actual medical analysis, the MedGemma model needs to be loaded.*"""
+        
+        else:
+            return f"""**DEMO MODE - Model not loaded**
+
+Thank you for describing your symptoms. In demo mode, I can provide general guidance:
+
+**General Recommendations:**
+1. Keep track of when symptoms started
+2. Note any factors that make symptoms better or worse
+3. Stay hydrated and get adequate rest
+4. Consider consulting a healthcare provider
+
+**When to Seek Medical Attention:**
+- Symptoms persist or worsen
+- You develop new concerning symptoms
+- You have underlying health conditions
+- You're unsure about the severity
+
+For a proper AI-powered analysis of your specific symptoms: "{symptoms_text[:100]}...", the MedGemma model would need to be successfully loaded.
+
+*Note: This is a demo response. For actual medical analysis, the MedGemma model needs to be loaded.*"""
+
+# Initialize the analyzer
+analyzer = MedGemmaSymptomAnalyzer()
+
+def analyze_symptoms_interface(symptoms, temperature):
+    """Interface function for Gradio"""
+    if not symptoms.strip():
+        return "Please enter some symptoms to analyze."
+    
+    # Add medical disclaimer
+    disclaimer = """⚠️ **MEDICAL DISCLAIMER**: This analysis is for educational purposes only and should not replace professional medical advice. Always consult with a healthcare provider for proper diagnosis and treatment.
+
+"""
+    
+    analysis = analyzer.analyze_symptoms(symptoms, temperature=temperature)
+    return disclaimer + analysis
+
+# Create Gradio interface
+with gr.Blocks(title="MedGemma Symptom Analyzer", theme=gr.themes.Soft()) as demo:
+    gr.Markdown("""
+    # 🏥 MedGemma Symptom Analyzer
+    
+    This application uses Google's MedGemma model to provide preliminary analysis of medical symptoms. 
+    Enter your symptoms below to get AI-powered insights.
+    
+    **Important**: This tool is for educational purposes only and should not replace professional medical advice.
+    """)
+    
+    with gr.Row():
+        with gr.Column(scale=2):
+            symptoms_input = gr.Textbox(
+                label="Describe your symptoms",
+                placeholder="Example: I have been experiencing headaches, fever, and fatigue for the past 3 days...",
+                lines=5,
+                max_lines=10
+            )
+            
+            temperature_slider = gr.Slider(
+                minimum=0.1,
+                maximum=1.0,
+                value=0.7,
+                step=0.1,
+                label="Response Creativity (Temperature)",
+                info="Lower values = more focused, Higher values = more creative"
+            )
+            
+            analyze_btn = gr.Button("🔍 Analyze Symptoms", variant="primary")
+            
+        with gr.Column(scale=3):
+            output = gr.Textbox(
+                label="Medical Analysis",
+                lines=15,
+                max_lines=20,
+                interactive=False
+            )
+    
+    # Example symptoms
+    gr.Markdown("### 📝 Example Symptoms to Try:")
+    with gr.Row():
+        example1 = gr.Button("Flu-like symptoms", size="sm")
+        example2 = gr.Button("Chest pain", size="sm")
+        example3 = gr.Button("Digestive issues", size="sm")
+    
+    # Event handlers
+    analyze_btn.click(
+        analyze_symptoms_interface,
+        inputs=[symptoms_input, temperature_slider],
+        outputs=output
+    )
+    
+    example1.click(
+        lambda: "I have been experiencing fever, body aches, headache, and fatigue for the past 2 days. I also have a slight cough.",
+        outputs=symptoms_input
+    )
+    
+    example2.click(
+        lambda: "I'm experiencing chest pain that worsens when I take deep breaths. It started this morning and is accompanied by shortness of breath.",
+        outputs=symptoms_input
+    )
+    
+    example3.click(
+        lambda: "I have been having stomach pain, nausea, and diarrhea for the past day. I also feel bloated and have lost my appetite.",
+        outputs=symptoms_input
+    )
+    
+    gr.Markdown("""
+    ### ⚠️ Important Notes:
+    - This is an AI tool for educational purposes only
+    - Always consult healthcare professionals for medical concerns
+    - Seek immediate medical attention for severe or emergency symptoms
+    - The AI may not always provide accurate medical information
+    """)
+
+if __name__ == "__main__":
+    demo.launch()

requirements.txt

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+gradio>=4.0.0
+torch>=2.0.0
+transformers>=4.35.0
+accelerate>=0.25.0
+bitsandbytes>=0.41.0
+sentencepiece>=0.1.99
+protobuf>=3.20.0