Update app.py

app.py

@@ -1,118 +1,50 @@
-import os
 import gradio as gr
+import os
 from groq import Groq
-import io
-import base64
-import logging
-from typing import Dict, Any
-
-from dotenv import load_dotenv
-from flask import Flask, request, jsonify
-from huggingface_hub import login
-import requests
-from PIL import Image
-from werkzeug.utils import secure_filename
-
-# Configure logging
-logging.basicConfig(level=logging.INFO)
-logger = logging.getLogger(__name__)
-
-# Load environment variables
-load_dotenv()
 
-# Initialize Flask app
-app = Flask(__name__)
+# Initialize Groq client
+client = Groq(
+    api_key=os.environ.get("GROQ_API_KEY"),
+)
 
-# Hugging Face and Groq API configuration
-HUGGINGFACE_TOKEN = os.environ.get("HUGGINGFACE_TOKEN")
-GROQ_API_URL = "https://api.groq.com/openai/v1/chat/completions"
-GROQ_API_KEY = os.environ.get("GROQ_API_KEY")
-
-# Login to Hugging Face
-if HUGGINGFACE_TOKEN:
-    login(HUGGINGFACE_TOKEN)
-else:
-    logger.warning("HUGGINGFACE_TOKEN not set. Some features may be limited.")
-
-# Define allowed file extensions
-ALLOWED_EXTENSIONS = {'png', 'jpg', 'jpeg'}
-
-def allowed_file(filename: str) -> bool:
-    return '.' in filename and filename.rsplit('.', 1)[1].lower() in ALLOWED_EXTENSIONS
-
-def groq_process(image: Image.Image) -> Dict[str, Any]:
-    """
-    Process the image using Groq's API for medical image analysis.
-    """
-    headers = {
-        "Authorization": f"Bearer {GROQ_API_KEY}",
-        "Content-Type": "application/json"
-    }
+def get_diagnosis(symptoms):
+    # IMPORTANT: This is a simplified example and should not be used for actual medical diagnosis
+    prompt = f"""
+    Given the following symptoms, provide a preliminary analysis of potential cancer risks. 
+    This is not a definitive diagnosis and should not replace professional medical advice.
     
-    # Convert image to base64
-    buffered = io.BytesIO()
-    image.save(buffered, format="PNG")
-    img_str = base64.b64encode(buffered.getvalue()).decode()
+    Symptoms: {symptoms}
     
-    payload = {
-        "model": "mixtral-8x7b-32768",
-        "messages": [
-            {"role": "system", "content": "You are an AI trained to analyze medical images. Provide a detailed analysis of the image, noting any abnormalities or potential areas of concern. Remember to state that this is an AI analysis and should be reviewed by a medical professional."},
-            {"role": "user", "content": f"Analyze this medical image in detail: data:image/png;base64,{img_str}"}
-        ],
-        "max_tokens": 500
-    }
+    Please provide:
+    1. Potential cancer types that might be associated with these symptoms
+    2. Recommendations for further medical tests or consultations
+    3. General health advice
     
-    try:
-        response = requests.post(GROQ_API_URL, headers=headers, json=payload, timeout=30)
-        response.raise_for_status()
-        return {"success": True, "analysis": response.json()['choices'][0]['message']['content']}
-    except requests.exceptions.RequestException as e:
-        logger.error(f"Error in Groq API request: {str(e)}")
-        return {"success": False, "error": "Error in processing the image"}
-
-def analyze_medical_image(image_file) -> Dict[str, Any]:
-    """
-    Analyze a medical image file.
-    """
-    try:
-        image = Image.open(image_file)
-        analysis_result = groq_process(image)
-        return analysis_result
-    except Exception as e:
-        logger.error(f"Error in image analysis: {str(e)}")
-        return {"success": False, "error": "Error in analyzing the image"}
-
-@app.route('/analyze-medical-image', methods=['POST'])
-def analyze_route():
-    """
-    Route to handle medical image analysis requests.
+    Remember to emphasize the importance of consulting with a medical professional for accurate diagnosis and treatment.
     """
-    if 'image' not in request.files:
-        return jsonify({"error": "No image file provided"}), 400
     
-    file = request.files['image']
-    if file.filename == '':
-        return jsonify({"error": "No selected file"}), 400
+    chat_completion = client.chat.completions.create(
+        messages=[
+            {
+                "role": "user",
+                "content": prompt,
+            }
+        ],
+        model="mixtral-8x7b-32768",
+        temperature=0.5,
+        max_tokens=1000,
+    )
     
-    if file and allowed_file(file.filename):
-        filename = secure_filename(file.filename)
-        analysis_result = analyze_medical_image(file)
-        
-        if analysis_result['success']:
-            return jsonify({
-                "filename": filename,
-                "analysis": analysis_result['analysis'],
-                "disclaimer": "This analysis is provided by an AI system and should be reviewed by a qualified medical professional. It is not a substitute for professional medical advice, diagnosis, or treatment."
-            })
-        else:
-            return jsonify({"error": analysis_result['error']}), 500
-    else:
-        return jsonify({"error": "Invalid file type. Allowed types are png, jpg, jpeg"}), 400
-
-@app.route('/', methods=['GET'])
-def index():
-    return "Medical Image Analysis Tool - For research and educational purposes only. Not for clinical use."
-
-if __name__ == "__main__":
-    app.run(host="0.0.0.0", port=7860)
+    return chat_completion.choices[0].message.content
+
+# Create Gradio interface
+iface = gr.Interface(
+    fn=get_diagnosis,
+    inputs=gr.Textbox(lines=5, label="Enter symptoms (separated by commas)"),
+    outputs="text",
+    title="Cancer Risk Assessment Tool",
+    description="This tool provides a preliminary analysis based on symptoms. It is not a substitute for professional medical advice, diagnosis, or treatment.",
+)
+
+# Launch the app
+iface.launch())