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app.py

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+import gradio as gr
+import dotenv
+from PIL import Image
+
+dotenv.load_dotenv()
+
+from models.loader import ModelLoader
+from models.inference import run_inference
+from reporting.tutor import generate_socratic_assessment
+
+# Initialize Model Loader
+loader = ModelLoader()
+
+# Global state to hold results between steps
+current_ai_results = {}
+
+def process_clinical_assessment(image):
+    if image is None:
+        yield image, "Please upload a Chest X-Ray image first."
+        return
+        
+    yield image, "Loading Model and running CNN Inference (this may take a moment)..."
+    model = loader.load_model()
+    
+    if model is None:
+        yield image, "### Error\nModel failed to load. Please check console logs."
+        return
+        
+    results = run_inference(model, image)
+    
+    global current_ai_results
+    current_ai_results = results
+    
+    diagnosis_data = {
+        "modality": "X-Ray",
+        "probabilities": results["all_probabilities"]
+    }
+    
+    yield image, "Generating Medical Tutor Socratic Feedack..."
+    
+    stream_generator = generate_socratic_assessment(diagnosis_data)
+    
+    for partial_text in stream_generator:
+        yield image, partial_text
+
+def reveal_ai_analysis():
+    global current_ai_results
+    if not current_ai_results:
+        return None, "No results yet. Please run the Clinical Assessment first.", {}
+        
+    heatmap = current_ai_results.get("heatmap_image", None)
+    confidence = current_ai_results.get("confidence", 0.0)
+    probs = current_ai_results.get("all_probabilities", {})
+    
+    top_diagnosis = current_ai_results.get("top_diagnosis", "Unknown")
+    confidence_text = f"## Top Diagnosis: **{top_diagnosis}** ({confidence*100:.1f}%)\n\nReview the heatmap to audit for spatial mismatch."
+    
+    return heatmap, confidence_text, probs
+
+def log_audit(audit_status, user_notes):
+    if not audit_status:
+        return "Please select an Audit Status."
+    return f"Audit Logged successfully!\n\nStatus: {audit_status}\nNotes: {user_notes}\n\nProceed to the next case to continue your education."
+
+with gr.Blocks(theme=gr.themes.Soft(), title="AI-VECINNA") as demo:
+    gr.Markdown("# AI-VECINNA: Dual-Model Medical Auditing (Powered by Local MedGemma)")
+    gr.Markdown("**Status: Running on NVIDIA T4 GPU (Persistent)**")
+    gr.Markdown("Welcome! This system aims to train you in identifying discrepancies between AI predictions (heatmaps/probabilities) and your clinical knowledge. Remember, AI is fallible; you are the human-in-the-loop.")
+    
+    with gr.Accordion("Step 1: Clinical Assessment & Tutor", open=True):
+        with gr.Row():
+            with gr.Column():
+                image_input = gr.Image(type="pil", label="Upload Chest X-Ray")
+                analyze_btn = gr.Button("Analyze & Generate Tutor Scenario", variant="primary")
+                
+            with gr.Column():
+                gr.Markdown("### MedGemma Socratic Tutor Feedback")
+                scenario_output = gr.Markdown("Tutor instructions and questions will appear here after analysis.")
+                
+                gr.Markdown("### Your Human Hypothesis")
+                human_hypothesis = gr.Textbox(label="Record your initial differential diagnosis here...", lines=3)
+                
+    with gr.Accordion("Step 2: AI Reveal & Audit (The Safety Standard)", open=False):
+        reveal_btn = gr.Button("Reveal AI Analysis", variant="secondary")
+        
+        with gr.Row():
+            with gr.Column():
+                heatmap_output = gr.Image(type="pil", label="GradCAM AI Heatmap")
+            with gr.Column():
+                confidence_output = gr.Markdown("Confidence results will appear here.")
+                probs_output = gr.Label(label="Full Probability Distribution")
+                
+        gr.Markdown("### Audit Form")
+        audit_radio = gr.Radio(
+            label="AI Safety Audit (Human-in-the-Loop Validation)",
+            choices=[
+                "AI Verified: Heatmap & Confidence clinically align.",
+                "AI Flagged: Spatial mismatch (Hallucination).",
+                "AI Flagged: Low confidence/over-reliance risk."
+            ]
+        )
+        audit_notes = gr.Textbox(label="Additional Audit Notes", lines=2)
+        audit_btn = gr.Button("Submit Safety Audit", variant="primary")
+        
+        audit_result = gr.Textbox(label="Audit Submission Status", interactive=False)
+        
+    # Wiring with loading state
+    analyze_btn.click(
+        fn=lambda: gr.update(interactive=False, value="Analyzing & Generating (Streaming)..."),
+        inputs=None,
+        outputs=analyze_btn
+    ).then(
+        fn=process_clinical_assessment,
+        inputs=[image_input],
+        outputs=[image_input, scenario_output]
+    ).then(
+        fn=lambda: gr.update(interactive=True, value="Analyze & Generate Tutor Scenario"),
+        inputs=None,
+        outputs=analyze_btn
+    )
+    
+    reveal_btn.click(
+        fn=reveal_ai_analysis,
+        inputs=[],
+        outputs=[heatmap_output, confidence_output, probs_output]
+    )
+    
+    audit_btn.click(
+        fn=log_audit,
+        inputs=[audit_radio, audit_notes],
+        outputs=audit_result
+    )
+
+if __name__ == "__main__":
+    demo.launch()

requirements.txt

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+torch
+torchvision
+torchxrayvision
+grad-cam
+gradio
+Pillow
+numpy
+python-dotenv
+transformers
+accelerate
+huggingface_hub

test_access.py

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+from transformers import AutoConfig; import os; from dotenv import load_dotenv; load_dotenv(); print("Testing access..."); config = AutoConfig.from_pretrained("google/medgemma-1.5-4b-it", token=os.getenv("HF_TOKEN")); print("Access Verified: ", config.model_type)

test_load.py

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+import os
+import torch
+from transformers import AutoTokenizer, AutoModelForCausalLM
+from dotenv import load_dotenv
+
+load_dotenv()
+hf_token = os.getenv("HF_TOKEN")
+model_id = "google/medgemma-1.5-4b-it"
+
+print(f"Testing HF_TOKEN: {hf_token[:5]}...{hf_token[-5:] if hf_token else 'None'}")
+print(f"Model ID: {model_id}")
+
+try:
+    print("Attempting to load tokenizer...")
+    tokenizer = AutoTokenizer.from_pretrained(model_id, token=hf_token)
+    print("Tokenizer loaded successfully.")
+    
+    print("Attempting to load model config (not weights yet)...")
+    model = AutoModelForCausalLM.from_pretrained(
+        model_id, 
+        token=hf_token,
+        torch_dtype=torch.bfloat16,
+        device_map="cpu",
+        low_cpu_mem_usage=True
+    )
+    print("Model loaded successfully.")
+except Exception as e:
+    print(f"DIAGNOSTIC FAILURE: {e}")