import os
import gradio as gr
import google.generativeai as genai

# Configure the Gemini API with environment variable
GOOGLE_API_KEY = os.getenv("GOOGLE_API_KEY")
if not GOOGLE_API_KEY:
    raise ValueError("GOOGLE_API_KEY environment variable not set. Please configure it in the Hugging Face Space settings.")

genai.configure(api_key=GOOGLE_API_KEY)

# Use Gemini 1.5 Flash model
model = genai.GenerativeModel('gemini-1.5-flash-latest')

def predict_tumor(headaches, neurological_symptoms, family_history, weight_loss, fatigue, nausea, balance_issues, speech_difficulties, mood_changes, radiation_exposure):
    # Validate inputs
    inputs = [headaches, neurological_symptoms, family_history, weight_loss, fatigue, nausea, balance_issues, speech_difficulties, mood_changes, radiation_exposure]
    if not all(inputs):
        return "Error: Please provide answers to all questions."
    
    # Format user responses
    user_data = f"""
    Persistent Headaches: {headaches}
    Neurological Symptoms (seizures, vision/hearing changes, cognitive issues): {neurological_symptoms}
    Family History of Cancer/Tumors: {family_history}
    Unexplained Weight Loss: {weight_loss}
    Persistent Fatigue: {fatigue}
    Unexplained Nausea or Vomiting: {nausea}
    Balance or Coordination Issues: {balance_issues}
    Speech Difficulties: {speech_difficulties}
    Personality or Mood Changes: {mood_changes}
    History of Radiation Exposure: {radiation_exposure}
    """

    prompt = f"""
    You are a health assistant predicting the likelihood of a tumor (e.g., brain tumor) based on user responses to 10 health questions. 
    Classify the risk as "Tumor Detected" or "No Tumor" and provide a brief reason for your classification.
    Return the response in this format:
    
    Prediction: [Tumor Detected / No Tumor]
    Reason: [Brief explanation]

    Examples:
    User Data:
    Persistent Headaches: Frequent and severe
    Neurological Symptoms (seizures, vision/hearing changes, cognitive issues): Yes
    Family History of Cancer/Tumors: Yes
    Unexplained Weight Loss: Yes
    Persistent Fatigue: Yes
    Unexplained Nausea or Vomiting: Yes
    Balance or Coordination Issues: Yes
    Speech Difficulties: Yes
    Personality or Mood Changes: Yes
    History of Radiation Exposure: Yes
    Prediction: Tumor Detected
    Reason: Multiple symptoms including frequent severe headaches, neurological symptoms, nausea, balance issues, speech difficulties, mood changes, along with family history, weight loss, fatigue, and radiation exposure, strongly suggest a potential tumor.

    User Data:
    Persistent Headaches: None
    Neurological Symptoms (seizures, vision/hearing changes, cognitive issues): No
    Family History of Cancer/Tumors: No
    Unexplained Weight Loss: No
    Persistent Fatigue: No
    Unexplained Nausea or Vomiting: No
    Balance or Coordination Issues: No
    Speech Difficulties: No
    Personality or Mood Changes: No
    History of Radiation Exposure: No
    Prediction: No Tumor
    Reason: The absence of symptoms such as headaches, neurological issues, nausea, balance problems, speech difficulties, mood changes, and risk factors like family history or radiation exposure suggests a low likelihood of a tumor.

    User Data:
    Persistent Headaches: Occasional and mild
    Neurological Symptoms (seizures, vision/hearing changes, cognitive issues): No
    Family History of Cancer/Tumors: Yes
    Unexplained Weight Loss: No
    Persistent Fatigue: Yes
    Unexplained Nausea or Vomiting: No
    Balance or Coordination Issues: No
    Speech Difficulties: No
    Personality or Mood Changes: No
    History of Radiation Exposure: No
    Prediction: No Tumor
    Reason: Mild headaches, fatigue, and family history alone, without other significant symptoms like neurological issues or nausea, suggest a low likelihood of a tumor.

    User Data:
    {user_data}
    Prediction:
    Reason:
    """

    try:
        response = model.generate_content(prompt)
        return response.text.strip()
    except Exception as e:
        return f"Error: {str(e)}\nTip: Ensure your API key is valid at https://aistudio.google.com/"

# Define Gradio interface
iface = gr.Interface(
    fn=predict_tumor,
    inputs=[
        gr.Dropdown(choices=["Frequent and severe", "Occasional and mild", "None"], label="1. Do you experience persistent headaches?"),
        gr.Dropdown(choices=["Yes", "No"], label="2. Do you have neurological symptoms (e.g., seizures, vision/hearing changes, cognitive issues)?"),
        gr.Dropdown(choices=["Yes", "No"], label="3. Do you have a family history of cancer or tumors?"),
        gr.Dropdown(choices=["Yes", "No"], label="4. Have you experienced unexplained weight loss?"),
        gr.Dropdown(choices=["Yes", "No"], label="5. Do you experience persistent fatigue or weakness?"),
        gr.Dropdown(choices=["Yes", "No"], label="6. Do you experience unexplained nausea or vomiting?"),
        gr.Dropdown(choices=["Yes", "No"], label="7. Do you have balance or coordination issues (e.g., difficulty walking)?"),
        gr.Dropdown(choices=["Yes", "No"], label="8. Do you have speech difficulties (e.g., slurred speech)?"),
        gr.Dropdown(choices=["Yes", "No"], label="9. Have you noticed personality or mood changes?"),
        gr.Dropdown(choices=["Yes", "No"], label="10. Have you been exposed to significant radiation (e.g., medical treatments, environmental)?")
    ],
    outputs=gr.Textbox(label="Tumor Detection Prediction"),
    title="Tumor Risk Predictor",
    description="Answer 10 health questions to predict the likelihood of a tumor using the Gemini API. Set your GOOGLE_API_KEY in the Hugging Face Space settings. Note: This is for demonstration purposes only and not a clinical tool."
)

# Launch the interface
if __name__ == "__main__":
    iface.launch()