Create app.py

app.py

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+import gradio as gr
+from transformers import pipeline
+import pandas as pd
+import PyPDF2
+import pdfplumber
+import torch
+import timm
+from PIL import Image
+
+# Load pre-trained model for zero-shot classification
+classifier = pipeline("zero-shot-classification", model="facebook/bart-large-mnli")
+
+# Pre-trained model for X-ray analysis (example with a model from timm library)
+image_model = timm.create_model('resnet50', pretrained=True)
+image_model.eval()
+
+# Initialize patient database
+patients_db = []
+
+# Disease and medication mapping
+disease_details = {
+    "anemia": {
+        "medication": "Iron supplements (e.g., Ferrous sulfate)",
+        "precaution": "Increase intake of iron-rich foods like spinach and red meat."
+    },
+    "viral infection": {
+        "medication": "Antiviral drugs (e.g., Oseltamivir for flu)",
+        "precaution": "Rest, stay hydrated, and avoid close contact with others."
+    },
+    "liver disease": {
+        "medication": "Hepatoprotective drugs (e.g., Ursodeoxycholic acid)",
+        "precaution": "Avoid alcohol and maintain a balanced diet."
+    },
+    "kidney disease": {
+        "medication": "Angiotensin-converting enzyme inhibitors (e.g., Lisinopril)",
+        "precaution": "Monitor salt intake and stay hydrated."
+    },
+    "diabetes": {
+        "medication": "Metformin or insulin therapy",
+        "precaution": "Follow a low-sugar diet and exercise regularly."
+    },
+    "hypertension": {
+        "medication": "Antihypertensive drugs (e.g., Amlodipine)",
+        "precaution": "Reduce salt intake and manage stress."
+    },
+    "COVID-19": {
+        "medication": "Supportive care, antiviral drugs (e.g., Remdesivir in severe cases)",
+        "precaution": "Follow isolation protocols, wear a mask, and stay hydrated."
+    },
+    "pneumonia": {
+        "medication": "Antibiotics (e.g., Amoxicillin) if bacterial",
+        "precaution": "Rest, avoid smoking, and stay hydrated."
+    }
+}
+
+# Function to register patients
+def register_patient(name, age, gender):
+    patient_id = len(patients_db) + 1
+    patients_db.append({
+        "ID": patient_id, 
+        "Name": name, 
+        "Age": age, 
+        "Gender": gender, 
+        "Symptoms": "", 
+        "Diagnosis": "", 
+        "Action Plan": "", 
+        "Medications": "", 
+        "Precautions": "",
+        "Tests": ""
+    })
+    return f"✅ Patient {name} registered successfully. Patient ID: {patient_id}"
+
+# Function to analyze text reports
+def analyze_report(patient_id, report_text):
+    candidate_labels = list(disease_details.keys())
+    result = classifier(report_text, candidate_labels)
+    diagnosis = result['labels'][0]
+
+    # Fetch medication and precaution
+    medication = disease_details[diagnosis]["medication"]
+    precaution = disease_details[diagnosis]["precaution"]
+    action_plan = f"You might have {diagnosis}. Please consult a doctor for confirmation."
+
+    # Store diagnosis in the database
+    for patient in patients_db:
+        if patient["ID"] == patient_id:
+            patient["Diagnosis"] = diagnosis
+            patient["Action Plan"] = action_plan
+            patient["Medications"] = medication
+            patient["Precautions"] = precaution
+            break
+    
+    return (f"🔍 Diagnosis: {diagnosis}\n"
+            f"🩺 Medications: {medication}\n"
+            f"⚠️ Precautions: {precaution}\n"
+            f"💡 {action_plan}")
+
+# Function to extract text from PDF reports
+def extract_pdf_report(pdf):
+    text = ""
+    with pdfplumber.open(pdf.name) as pdf_file:
+        for page in pdf_file.pages:
+            text += page.extract_text()
+    return text
+
+# Function to analyze uploaded images (X-ray/CT-scan)
+def analyze_image(patient_id, img):
+    image = Image.open(img).convert('RGB')
+    transform = torch.nn.Sequential(
+        torch.nn.Upsample(size=(224, 224)),
+        torch.nn.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])
+    )
+    image_tensor = transform(torch.unsqueeze(torch.tensor(image), 0))
+
+    # Run the image through the model (for simplicity, assuming ResNet50 output)
+    output = image_model(image_tensor)
+    _, predicted = torch.max(output, 1)
+
+    # Map prediction to a label
+    labels = {0: "Normal", 1: "Pneumonia", 2: "Liver Disorder", 3: "COVID-19"}
+    diagnosis = labels.get(predicted.item(), "Unknown")
+
+    # Store diagnosis in the database
+    for patient in patients_db:
+        if patient["ID"] == patient_id:
+            patient["Diagnosis"] = diagnosis
+            break
+    
+    return f"🔍 Diagnosis from image: {diagnosis}"
+
+# Function to display the dashboard
+def show_dashboard():
+    if not patients_db:
+        return "No patient records available."
+    return pd.DataFrame(patients_db)
+
+# Text-to-Image model interface
+def generate_image(prompt):
+    # This assumes you're using a pre-trained model for text-to-image generation
+    return gr.load("models/ZB-Tech/Text-to-Image").launch()
+
+# Gradio interface for patient registration
+patient_interface = gr.Interface(
+    fn=register_patient,
+    inputs=[
+        gr.Textbox(label="Patient Name", placeholder="Enter the patient's full name"),
+        gr.Number(label="Age"),  
+        gr.Radio(label="Gender", choices=["Male", "Female", "Other"])
+    ],
+    outputs="text",
+    description="Register a new patient"
+)
+
+# Gradio interface for report analysis (text input)
+report_interface = gr.Interface(
+    fn=analyze_report,
+    inputs=[
+        gr.Number(label="Patient ID"),  
+        gr.Textbox(label="Report Text", placeholder="Paste the text from your report here")
+    ],
+    outputs="text",
+    description="Analyze blood, LFT, or other medical reports"
+)
+
+# Gradio interface for PDF report analysis (PDF upload)
+pdf_report_interface = gr.Interface(
+    fn=extract_pdf_report,
+    inputs=gr.File(label="Upload PDF Report"),
+    outputs="text",
+    description="Extract and analyze text from PDF reports"
+)
+
+# Gradio interface for X-ray/CT-scan image analysis
+image_interface = gr.Interface(
+    fn=analyze_image,
+    inputs=[
+        gr.Number(label="Patient ID"),
+        gr.Image(type="filepath", label="Upload X-ray or CT-Scan Image")
+    ],
+    outputs="text",
+    description="Analyze X-ray or CT-scan images for diagnosis"
+)
+
+# Gradio interface for the dashboard
+dashboard_interface = gr.Interface(
+    fn=show_dashboard,
+    inputs=None,
+    outputs="dataframe",
+    description="View patient reports and history"
+)
+
+# Gradio interface for text-to-image
+text_to_image_interface = gr.Interface(
+    fn=generate_image,
+    inputs=gr.Textbox(label="Enter a prompt to generate an image"),
+    outputs="image",
+    description="Generate images from text prompts"
+)
+
+# Organize the layout using Blocks
+with gr.Blocks() as demo:
+    gr.Markdown("# Medical Report and Image Analyzer + Text-to-Image Generator")
+    choice = gr.Radio(label="Choose Functionality", choices=["Medical Reports", "Text-to-Image"], value="Medical Reports")
+    
+    with gr.Column():
+        with gr.TabItem("Patient Registration"):
+            patient_interface.render()
+        with gr.TabItem("Analyze Report (Text)"):
+            report_interface.render()
+        with gr.TabItem("Analyze Report (PDF)"):
+            pdf_report_interface.render()
+        with gr.TabItem("Analyze Image (X-ray/CT)"):
+            image_interface.render()
+        with gr.TabItem("Dashboard"):
+            dashboard_interface.render()
+        with gr.TabItem("Text-to-Image"):
+            text_to_image_interface.render()
+
+demo.launch(share=True)