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6d22265 5de2833 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 | import os
import google.generativeai as genai
import gradio as gr
from dotenv import load_dotenv
from PIL import Image
# 1. SETUP API KEY
# ----------------
# Load environment variables from the .env file (for local development)
load_dotenv()
# Fetch the key. If running on Hugging Face, it will look in their 'Secrets'.
api_key = os.getenv("GEMINI_API_KEY")
if not api_key:
raise ValueError("API Key not found. Please set GEMINI_API_KEY in .env or Secrets.")
# Configure the Google AI library
genai.configure(api_key=api_key)
def analyze_symptoms(text_input, image_input , temperature=0.7, top_p=0.6 ):
"""
This function takes text and an image, sends them to Gemini,
and returns the medical analysis.
"""
# Validation: Ensure at least one input is provided
if not text_input and not image_input:
return "Please provide a description or an image."
# Initialize the model
# 'gemini-1.5-flash' is excellent for multimodal tasks (fast & accurate).
model = genai.GenerativeModel('gemini-2.5-flash')
generation_config = genai.types.GenerationConfig(
temperature=temperature,
top_p=top_p
)
# Create the System Prompt
# We must instruct the AI to act like a doctor but be safe.
prompt_text = (
"You are an AI medical assistant. "
"Analyze the following symptoms and the provided image (if any). "
"Provide potential causes and home remedies. "
"IMPORTANT: You must start your response with a clear disclaimer "
"that you are an AI and this is not professional medical advice."
f"\n\nPatient Description: {text_input}"
)
# Prepare the content for Gemini
# Gemini accepts a list containing text and/or image data.
content = [prompt_text]
if image_input:
content.append(image_input)
try:
# Generate content
response = model.generate_content(content , generation_config=generation_config)
return response.text
except Exception as e:
return f"Error: {str(e)}"
# Blocks allows us to build complex layouts (Rows, Columns, etc.)
with gr.Blocks(theme=gr.themes.Soft()) as demo:
# Header
gr.Markdown("# 🏥 AI Health Symptom Checker")
gr.Markdown("Describe your symptoms and upload an image (e.g., a rash, sore throat) for preliminary analysis.")
# Layout: Use a Row to put inputs side-by-side (on large screens)
with gr.Row():
# Left Column: Inputs
with gr.Column():
symptoms = gr.Textbox(
label="Describe your symptoms",
placeholder="E.g., I've had a headache and sore throat for 2 days...",
lines=4
)
# The type='pil' ensures the image is ready for the Python Pillow library
img_upload = gr.Image(label="Upload Image (Optional)", type="pil")
# Custom Button
submit_btn = gr.Button("Analyze Symptoms", variant="primary")
# Right Column: Output
with gr.Column():
output_box = gr.Markdown("AI Analysis")
# 4. CONNECTING LOGIC (Event Listeners)
# -------------------------------------
# When the button is clicked, run 'analyze_symptoms'
# Inputs: [symptoms, img_upload] -> correspond to function arguments
# Outputs: [output_box] -> where the result goes
submit_btn.click(
fn=analyze_symptoms,
inputs=[symptoms, img_upload],
outputs=output_box
)
print("App is Launching...")
# 5. LAUNCH THE APP
# -----------------
demo.launch(share=True) |