Upload app.py

app.py

@@ -0,0 +1,125 @@
+import os
+import google.generativeai as genai
+import gradio as gr
+
+from dotenv import load_dotenv
+from PIL import Image
+##############commands######################
+#python3 -m venv venv
+#source venv/bin/activate
+#pip install google-generativeai gradio python-dotenv pillow
+###########################################
+# 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, top_p ):
+    """
+    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")
+           
+            # Configure the Google AI library
+            temperature_slider = gr.Slider(
+                minimum=0.0,
+                maximum=1.0,
+                value=0.7,
+                step=0.05,
+                label="🔥 Temperature (Creativity Level)"
+            )
+
+            top_p_slider = gr.Slider(
+                 minimum=0.0,
+                 maximum=1.0,
+                 value=0.9,
+                 step=0.05,
+                 label="🎯 Top-p (Response Diversity)"
+            )
+
+        # Right Column: Output
+        with gr.Column():
+            output_box = gr.Markdown(label="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, temperature_slider, top_p_slider], 
+        outputs=output_box
+    )
+print("App is  Launching...")
+# 5. LAUNCH THE APP
+# -----------------
+demo.launch()