Update app.py

app.py

@@ -1,3 +1,63 @@
-import gradio as gr
+from flask import Flask, render_template, request, redirect, url_for
+import requests
+from PIL import Image
+from io import BytesIO
+import base64
 
-gr.Interface.load("models/openai/clip-vit-base-patch32").launch()
+app = Flask(__name__)
+
+# Replace with your own API keys
+CLIP_API_KEY = "your_clip_api_key"
+STABLE_DIFFUSION_API_KEY = "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"
+
+@app.route('/')
+def index():
+    return render_template('index.html')
+
+@app.route('/generate', methods=['POST'])
+def generate():
+    image = request.files['image']
+    mood = get_mood_from_image(image)
+    
+    if mood:
+        art, narrative = generate_art_and_narrative(mood)
+        return render_template('result.html', art=art, narrative=narrative)
+    else:
+        return redirect(url_for('index'))
+
+def get_mood_from_image(image):
+    # Implement mood classification logic using the CLIP API
+    moods = ["happy", "sad", "angry", "neutral"]
+    prompt = "The mood of the person in this image is: "
+    
+    headers = {
+        "Authorization": f"Bearer {CLIP_API_KEY}"
+    }
+    
+    # Convert the image to base64
+    image_base64 = base64.b64encode(image.read()).decode('utf-8')
+    
+    json_data = {
+        "inputs": [{"data": {"image": {"base64": image_base64}}, "prompt": prompt} for mood in moods]
+    }
+    
+    response = requests.post('https://api-inference.huggingface.co/models/openai/clip-vit-base-patch32', headers=headers, json=json_data).json()
+
+    mood_scores = {}
+    for choice, mood in zip(response, moods):
+        mood_scores[mood] = float(choice['scores'][0])
+
+    # Filter moods with a score above 60%
+    filtered_moods = {k: v for k, v in mood_scores.items() if v > 0.6}
+
+    if len(filtered_moods) < 2:
+        return None
+
+    return filtered_moods
+
+def generate_art_and_narrative(mood):
+    # Implement art generation logic using the Stable Diffusion API
+    pass
+
+if __name__ == '__main__':
+    app.run(debug=True)