Create app.py

app.py

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+import gradio as gr
+import torch
+from transformers import AutoTokenizer, AutoModelForSequenceClassification, AutoImageProcessor, AutoModelForImageClassification
+from PIL import Image
+import json
+import re
+import pandas as pd
+from datetime import datetime
+import plotly.express as px
+from io import StringIO
+
+# Load text model
+text_model_name = "microsoft/BiomedVLP-CXR-BERT-specialized"
+text_tokenizer = AutoTokenizer.from_pretrained(text_model_name)
+text_model = AutoModelForSequenceClassification.from_pretrained(text_model_name)
+
+# Load image model
+image_model_name = "aehrc/cxrmate-tf"  # Replace with skin disease model if needed
+image_processor = AutoImageProcessor.from_pretrained(image_model_name)
+image_model = AutoModelForImageClassification.from_pretrained(image_model_name)
+
+# Define labels
+text_labels = ["Positive", "Negative", "Neutral", "Informative"]  # For text analysis
+image_labels = ["Normal", "Abnormal"]  # For X-ray or skin images
+
+# Store conversation state
+conversation_state = {
+    "history": [],
+    "texts": [],
+    "image_uploaded": False,
+    "last_analysis": None,
+    "analysis_log": []
+}
+
+# Extract key terms
+def extract_key_terms(text):
+    terms = re.findall(r'\b(fever|cough|fatigue|headache|sore throat|chest pain|shortness of breath|rash|lesion|study|treatment|trial|astronaut|microgravity)\b', text, re.IGNORECASE)
+    return terms
+
+# Generate context-aware follow-up questions
+def generate_follow_up(terms, history):
+    if not terms:
+        return "Please provide medical text (e.g., symptoms, abstract) or upload an image."
+    if "astronaut" in [t.lower() for t in terms] or "microgravity" in [t.lower() for t in terms]:
+        return "Are you researching space medicine? Please describe physiological data or symptoms in microgravity."
+    if len(terms) < 3:
+        return "Can you provide more details (e.g., duration of symptoms or study context)?"
+    if not conversation_state["image_uploaded"]:
+        return "Would you like to upload an image (e.g., X-ray or skin photo) for analysis?"
+    return "Would you like to analyze another text or image, or export the analysis log?"
+
+# Main analysis function
+def analyze_medical_input(user_input, image=None, chat_history=None, export_format="None"):
+    global conversation_state
+    if not chat_history:
+        chat_history = []
+
+    # Process text input
+    text_response = ""
+    text_chart = ""
+    if user_input.strip():
+        terms = extract_key_terms(user_input)
+        conversation_state["texts"].extend(terms)
+        inputs = text_tokenizer(user_input, return_tensors="pt", padding=True, truncation=True, max_length=512)
+        with torch.no_grad():
+            outputs = text_model(**inputs)
+        logits = outputs.logits
+        predicted_class_idx = logits.argmax(-1).item()
+        confidence = torch.softmax(logits, dim=-1)[0][predicted_class_idx].item()
+        scores = torch.softmax(logits, dim=-1)[0].tolist()
+        conversation_state["last_analysis"] = {
+            "type": "text",
+            "label": text_labels[predicted_class_idx],
+            "confidence": confidence,
+            "scores": scores,
+            "input": user_input,
+            "timestamp": datetime.now().strftime("%Y-%m-%d %H:%M:%S")
+        }
+        conversation_state["analysis_log"].append(conversation_state["last_analysis"])
+        text_response = f"Text Analysis: {text_labels[predicted_class_idx]} (Confidence: {confidence:.2%})"
+
+        # Text visualization (Chart.js)
+        chart_data = {
+            "type": "bar",
+            "data": {
+                "labels": text_labels,
+                "datasets": [{
+                    "label": "Confidence Scores",
+                    "data": scores,
+                    "backgroundColor": ["#4CAF50", "#F44336", "#2196F3", "#FF9800"],
+                    "borderColor": ["#388E3C", "#D32F2F", "#1976D2", "#F57C00"],
+                    "borderWidth": 1
+                }]
+            },
+            "options": {
+                "scales": {
+                    "y": {"beginAtZero": True, "max": 1, "title": {"display": True, "text": "Confidence"}},
+                    "x": {"title": {"display": True, "text": "Text Categories"}}
+                },
+                "plugins": {"title": {"display": True, "text": "Text Analysis Confidence"}}
+            }
+        }
+        text_chart = f"""
+        <canvas id='textChart' width='400' height='200'></canvas>
+        <script src='https://cdn.jsdelivr.net/npm/chart.js'></script>
+        <script>
+            new Chart(document.getElementById('textChart'), {json.dumps(chart_data)});
+        </script>
+        """
+
+    # Process image input
+    image_response = ""
+    image_chart = ""
+    if image is not None:
+        conversation_state["image_uploaded"] = True
+        inputs = image_processor(images=image, return_tensors="pt")
+        with torch.no_grad():
+            outputs = image_model(**inputs)
+        logits = outputs.logits
+        predicted_class_idx = logits.argmax(-1).item()
+        confidence = torch.softmax(logits, dim=-1)[0][predicted_class_idx].item()
+        scores = torch.softmax(logits, dim=-1)[0].tolist()
+        conversation_state["last_analysis"] = {
+            "type": "image",
+            "label": image_labels[predicted_class_idx],
+            "confidence": confidence,
+            "scores": scores,
+            "input": "image",
+            "timestamp": datetime.now().strftime("%Y-%m-%d %H:%M:%S")
+        }
+        conversation_state["analysis_log"].append(conversation_state["last_analysis"])
+        image_response = f"Image Analysis: {image_labels[predicted_class_idx]} (Confidence: {confidence:.2%})"
+
+        # Image visualization (Chart.js)
+        chart_data = {
+            "type": "bar",
+            "data": {
+                "labels": image_labels,
+                "datasets": [{
+                    "label": "Confidence Scores",
+                    "data": scores,
+                    "backgroundColor": ["#4CAF50", "#F44336"],
+                    "borderColor": ["#388E3C", "#D32F2F"],
+                    "borderWidth": 1
+                }]
+            },
+            "options": {
+                "scales": {
+                    "y": {"beginAtZero": True, "max": 1, "title": {"display": True, "text": "Confidence"}},
+                    "x": {"title": {"display": True, "text": "Image Categories"}}
+                },
+                "plugins": {"title": {"display": True, "text": "Image Analysis Confidence"}}
+            }
+        }
+        image_chart = f"""
+        <canvas id='imageChart' width='400' height='200'></canvas>
+        <script src='https://cdn.jsdelivr.net/npm/chart.js'></script>
+        <script>
+            new Chart(document.getElementById('imageChart'), {json.dumps(chart_data)});
+        </script>
+        """
+
+    # Generate trend visualization (Plotly)
+    trend_html = ""
+    if len(conversation_state["analysis_log"]) > 1:
+        df = pd.DataFrame(conversation_state["analysis_log"])
+        fig = px.line(
+            df, x="timestamp", y="confidence", color="type",
+            title="Analysis Confidence Over Time",
+            labels={"confidence": "Confidence Score", "timestamp": "Time"}
+        )
+        trend_html = fig.to_html(full_html=False)
+
+    # Combine responses
+    response = "\n".join([r for r in [text_response, image_response] if r])
+    if not response:
+        response = "No analysis yet. Please provide text or upload an image."
+    response += f"\n\nFollow-Up: {generate_follow_up(conversation_state['texts'], conversation_state['history'])}"
+    response += f"\n\n{text_chart}\n{image_chart}\n{trend_html}"
+
+    # Handle export
+    if export_format != "None":
+        df = pd.DataFrame(conversation_state["analysis_log"])
+        if export_format == "JSON":
+            export_data = df.to_json(orient="records")
+            return response, gr.File(value=StringIO(export_data), file_name="analysis_log.json")
+        elif export_format == "CSV":
+            export_data = df.to_csv(index=False)
+            return response, gr.File(value=StringIO(export_data), file_name="analysis_log.csv")
+
+    # Add disclaimer
+    disclaimer = "⚠️ This tool is for research purposes only and does not provide medical diagnoses. Consult a healthcare professional for medical advice."
+    response += f"\n\n{disclaimer}"
+
+    conversation_state["history"].append((user_input, response))
+    return response
+
+# Custom CSS for professional UI
+css = """
+body { background-color: #f0f2f5; font-family: 'Segoe UI', Arial, sans-serif; }
+.gradio-container { max-width: 900px; margin: auto; padding: 30px; background: white; border-radius: 10px; box-shadow: 0 4px 12px rgba(0,0,0,0.1); }
+h1 { color: #1a3c5e; text-align: center; font-size: 2em; }
+input, textarea { border-radius: 8px; border: 1px solid #ccc; padding: 10px; }
+button { background: linear-gradient(90deg, #3498db, #2980b9); color: white; border-radius: 8px; padding: 12px; font-weight: bold; }
+button:hover { background: linear-gradient(90deg, #2980b9, #1a6ea6); }
+#export_dropdown { width: 150px; margin-top: 10px; }
+"""
+
+# Create Gradio interface
+with gr.Blocks(css=css) as iface:
+    gr.Markdown("# Ultra-Advanced Medical Research Chatbot")
+    gr.Markdown("Analyze medical texts or images for research purposes. Supports symptom analysis, literature review, or space medicine research. Not for medical diagnosis.")
+    with gr.Row():
+        with gr.Column(scale=2):
+            text_input = gr.Textbox(lines=5, placeholder="Enter symptoms, medical abstract, or space medicine data...")
+            image_input = gr.Image(type="pil", label="Upload X-ray or Skin Image")
+            export_dropdown = gr.Dropdown(choices=["None", "JSON", "CSV"], label="Export Log", value="None")
+            submit_button = gr.Button("Analyze")
+        with gr.Column(scale=3):
+            output = gr.HTML()
+    submit_button.click(
+        fn=analyze_medical_input,
+        inputs=[text_input, image_input, gr.State(), export_dropdown],
+        outputs=[output, gr.File()]
+    )
+
+# Launch the interface
+iface.launch()