Update app.py

app.py

@@ -1,45 +1,56 @@
-import gradio as gr
+from flask import Flask, request, jsonify, send_file
 from analysis import get_comprehensive_analysis, generate_html_report
 from pdf_generator import generate_pdf
 import os
+import uuid
 
+app = Flask(__name__)
 
-def process(image_path):
 
-    # 1️⃣ Run AI analysis → THIS is the JSON you want to show
-    analysis = get_comprehensive_analysis(image_path)
-    if not analysis:
-        return None, None
+@app.route("/")
+def home():
+    return "YOUV.AI Flask Skin Analysis API is running!", 200
 
-    # 2️⃣ Generate HTML report using SAME analysis JSON
-    html_file = generate_html_report(analysis, "new_report.html")
-    html_file = os.path.abspath(html_file)
 
-    if not os.path.exists(html_file):
-        raise FileNotFoundError("HTML report not generated!")
+@app.route("/analyze", methods=["POST"])
+def analyze():
+    # Check if image is uploaded
+    if "image" not in request.files:
+        return jsonify({"success": False, "error": "No image uploaded"}), 400
 
-    # 3️⃣ Convert HTML → PDF
-    pdf_file = generate_pdf(html_file, "report.pdf")
+    image_file = request.files["image"]
+
+    # Temp file
+    temp_name = f"upload_{uuid.uuid4().hex}.jpg"
+    image_file.save(temp_name)
 
-    if not os.path.exists(pdf_file):
-        raise FileNotFoundError("PDF report not generated!")
+    # Run AI skin analysis → returns JSON!
+    analysis = get_comprehensive_analysis(temp_name)
+    if not analysis:
+        os.remove(temp_name)
+        return jsonify({"success": False, "error": "Analysis failed"}), 500
+
+    # Create report HTML + PDF
+    html_file = generate_html_report(analysis, "new_report.html")
+    pdf_file = generate_pdf(html_file, "report.pdf")
 
-    # 4️⃣ Return BOTH: raw JSON + PDF
-    return analysis, pdf_file
+    # Delete temp upload
+    os.remove(temp_name)
 
+    # If user wants PDF download directly
+    return_pdf = request.form.get("return_pdf", "false").lower() == "true"
+    
+    if return_pdf:
+        return send_file(pdf_file, mimetype="application/pdf", as_attachment=True)
 
-# ================================
-#   Gradio UI
-# ================================
+    # Otherwise return JSON + confirmation PDF exists
+    return jsonify({
+        "success": True,
+        "analysis": analysis,
+        "pdf_available": True
+    }), 200
 
-demo = gr.Interface(
-    fn=process,
-    inputs=gr.Image(type="filepath", label="Upload Your Image"),
-    outputs=[
-        gr.JSON(label="Raw JSON Used in Report"),
-        gr.File(label="Download Your PDF Report"),
-    ],
-    title="YOUV.AI Skin Analysis Report Generator"
-)
 
-demo.launch(server_name="0.0.0.0", server_port=7860)
+# REQUIRED for HuggingFace Spaces Docker
+if __name__ == "__main__":
+    app.run(host="0.0.0.0", port=7860)