Create app.py

app.py

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+import gradio as gr
+import time
+from collections import defaultdict
+import spacy
+
+# Load the symptom-to-disease mapping
+symptom_data = {
+    "Shortness of breath": {
+        "questions": [
+            "Do you also have chest pain?",
+            "Do you feel fatigued often?",
+            "Have you noticed swelling in your legs?"
+        ],
+        "diseases": ["Atelectasis", "Emphysema", "Edema"],
+        "weights_yes": [30, 30, 40],
+        "weights_no": [10, 20, 30]
+    },
+    "Persistent cough": {
+        "questions": [
+            "Is your cough dry or with mucus?",
+            "Do you experience fever?",
+            "Do you have difficulty breathing?"
+        ],
+        "diseases": ["Pneumonia", "Fibrosis", "Infiltration"],
+        "weights_yes": [35, 30, 35],
+        "weights_no": [10, 15, 20]
+    },
+    "Sharp chest pain": {
+        "questions": [
+            "Does it worsen with deep breaths?",
+            "Do you feel lightheaded?",
+            "Have you had recent trauma or surgery?"
+        ],
+        "diseases": ["Pneumothorax", "Effusion", "Cardiomegaly"],
+        "weights_yes": [40, 30, 30],
+        "weights_no": [15, 20, 25]
+    },
+    "Fatigue & swelling": {
+        "questions": [
+            "Do you feel breathless when lying down?",
+            "Have you gained weight suddenly?",
+            "Do you experience irregular heartbeat?"
+        ],
+        "diseases": ["Edema", "Cardiomegaly"],
+        "weights_yes": [50, 30, 20],
+        "weights_no": [20, 15, 15]
+    },
+    "Chronic wheezing": {
+        "questions": [
+            "Do you have a history of smoking?",
+            "Do you feel tightness in your chest?",
+            "Do you have frequent lung infections?"
+        ],
+        "diseases": ["Emphysema", "Fibrosis"],
+        "weights_yes": [40, 30, 30],
+        "weights_no": [15, 25, 20]
+    }
+}
+
+# Load spaCy model for NLP
+nlp = spacy.load("en_core_web_lg")
+
+# Function to extract key symptom from user input
+def extract_symptom(user_input):
+    # Define the symptoms that the chatbot recognizes
+    known_symptoms = list(symptom_data.keys())
+    
+    # Process the input with spaCy NLP model
+    user_doc = nlp(user_input.lower())
+    
+    # Check if any of the known symptoms are in the user input
+    for symptom in known_symptoms:
+        if symptom.lower() in user_input.lower():
+            return symptom
+    
+    # If no direct match, use similarity to find the closest symptom
+    similarities = {}
+    for symptom in known_symptoms:
+        symptom_doc = nlp(symptom.lower())
+        similarity = user_doc.similarity(symptom_doc)
+        similarities[symptom] = similarity
+    
+    # Return the symptom with the highest similarity
+    return max(similarities, key=similarities.get)
+
+# Mapping of unrecognized symptoms to similar known ones
+synonym_mapping = {
+    "chest pain": "Sharp chest pain",
+    "pain in chest": "Sharp chest pain",
+    "wheezing": "Chronic wheezing",
+    "cough": "Persistent cough",
+    "shortness of breath": "Shortness of breath",
+    "fatigue": "Fatigue & swelling"
+}
+
+# Global variables to track user state
+user_state = {}
+
+def chatbot(user_input):
+    if "state" not in user_state:
+        user_state["state"] = "greet"
+    
+    if user_state["state"] == "greet":
+        user_state["state"] = "ask_symptom"
+        return "Hello! I'm a medical AI assistant. Please describe your primary symptom."
+    
+    elif user_state["state"] == "ask_symptom":
+        # Check if the symptom contains any synonym or keyword
+        matched_symptom = None
+        
+        for synonym, recognized_symptom in synonym_mapping.items():
+            if synonym in user_input.lower():
+                matched_symptom = recognized_symptom
+                break
+        
+        # If no synonym found, extract the symptom using NLP
+        if not matched_symptom:
+            matched_symptom = extract_symptom(user_input)
+        
+        # If the symptom is recognized, proceed to the next step
+        if matched_symptom not in symptom_data:
+            user_state["state"] = "ask_feeling"
+            return "I'm sorry, I don't recognize that symptom. How do you feel?"
+        
+        user_state["symptom"] = matched_symptom
+        user_state["state"] = "ask_duration"
+        return "How long have you been experiencing this symptom? (Less than a week / More than a week)"
+    
+    elif user_state["state"] == "ask_feeling":
+        # If the symptom is not recognized, ask how they feel
+        return "Can you describe your symptoms in more detail?"
+    
+    elif user_state["state"] == "ask_duration":
+        if user_input.lower() == "less than a week":
+            user_state.clear()
+            return "It might be a temporary issue. Please monitor your symptoms and consult a doctor if they persist."
+        elif user_input.lower() == "more than a week":
+            user_state["state"] = "follow_up"
+            user_state["current_question"] = 0
+            user_state["disease_scores"] = defaultdict(int)
+            return symptom_data[user_state["symptom"]]["questions"][0]
+        else:
+            return "Please respond with 'Less than a week' or 'More than a week'."
+    
+    elif user_state["state"] == "follow_up":
+        symptom = user_state["symptom"]
+        question_index = user_state["current_question"]
+        
+        # Update probabilities
+        if user_input.lower() == "yes":
+            for i, disease in enumerate(symptom_data[symptom]["diseases"]):
+                user_state["disease_scores"][disease] += symptom_data[symptom]["weights_yes"][i]
+        else:
+            for i, disease in enumerate(symptom_data[symptom]["diseases"]):
+                user_state["disease_scores"][disease] += symptom_data[symptom]["weights_no"][i]
+        
+        # Move to the next question or finish
+        user_state["current_question"] += 1
+        if user_state["current_question"] < len(symptom_data[symptom]["questions"]):
+            return symptom_data[symptom]["questions"][user_state["current_question"]]
+        
+        # Final diagnosis
+        probable_disease = max(user_state["disease_scores"], key=user_state["disease_scores"].get)
+        user_state.clear()
+        return f"Based on your symptoms, the most likely condition is: {probable_disease}. Please consult a doctor for confirmation."
+
+# Gradio Chatbot UI with improved features
+with gr.Blocks() as demo:
+    gr.Markdown("# Conversational Image Recognition Assistant: AI-Powered X-ray Diagnosis for Healthcare")
+    chatbot_ui = gr.Chatbot()
+    user_input = gr.Textbox(placeholder="Enter your response...", label="Your Message")
+    submit = gr.Button("Send")
+    clear_chat = gr.Button("Clear Chat")
+    
+    def respond(user_message, history):
+        history.append((user_message, "Thinking..."))  # Show thinking message
+        yield history, ""  # Immediate update
+        
+        time.sleep(1.5)  # Simulate processing delay
+        bot_response = chatbot(user_message)
+        history[-1] = (user_message, bot_response)  # Update with real response
+        yield history, ""
+    
+    submit.click(respond, [user_input, chatbot_ui], [chatbot_ui, user_input])
+    user_input.submit(respond, [user_input, chatbot_ui], [chatbot_ui, user_input])
+    clear_chat.click(lambda: ([], ""), outputs=[chatbot_ui, user_input])
+
+demo.launch()