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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() |