Upload app.py

app.py

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+from flask import Flask, render_template, request, jsonify
+from gradio_client import Client, handle_file
+import os
+import json
+import random  # For demo data variation
+
+app = Flask(__name__)
+app.config['UPLOAD_FOLDER'] = 'uploads'
+app.config['MAX_CONTENT_LENGTH'] = 16 * 1024 * 1024  # 16 MB max upload size
+os.makedirs(app.config['UPLOAD_FOLDER'], exist_ok=True)
+
+
+API_TOKEN_HF = os.environ['API_TOKEN_HF']
+
+# --- Demo Data ---
+# This will be used for initial page load and if the API call fails or is mocked
+BASE_AROMA_NAMES_ORDER = [  # Keep this order consistent with frontend
+    "Rose", "Ocean Breeze", "Fresh Cut Grass", "Lemon Zest", "Lavender",
+    "Sweet Orange", "Cool Mint", "Vanilla Bean", "Wild Berry", "Spring Rain"
+]
+
+client = Client("doevent/gemini_others", hf_token=API_TOKEN_HF, download_files="uploads")
+
+def get_demo_perfume_data():
+    perfume_name = random.choice(["Demo Garden", "Demo Whisper", "Demo Flare", "Demo Dream"])
+    slogan = random.choice([
+        "Awaken your senses.", "Dare to dream.", "The essence of you.", "An unforgettable journey."
+    ])
+
+    # Create a random set of aromas and doses
+    num_active_aromas = random.randint(2, 10)
+    active_aromas_sample = random.sample(BASE_AROMA_NAMES_ORDER, num_active_aromas)
+
+    aromas_data = []
+    total_dose_for_demo = 0  # To simulate filling the flask
+    for i, name in enumerate(BASE_AROMA_NAMES_ORDER):
+        if name in active_aromas_sample and total_dose_for_demo < 0.8:  # Limit total demo dose
+            dose = round(random.uniform(0.1, 0.3), 2)
+            aromas_data.append({"name": name, "dose": dose})
+            total_dose_for_demo += dose
+        # else: # We can include all aromas with 0 dose if needed by frontend for table population
+        #     aromas_data.append({"name": name, "dose": 0.0})
+
+    return {
+        "perfume_name": perfume_name,
+        "slogan": slogan,
+        "aromas": aromas_data  # This list might only contain active aromas
+    }
+
+
+@app.route('/')
+def index():
+    initial_data = get_demo_perfume_data()
+    return render_template('index.html', initial_data=initial_data, base_aromas_ordered=BASE_AROMA_NAMES_ORDER)
+
+
+@app.route('/analyze_image', methods=['POST'])
+def analyze_image_route():
+    if 'imageFile' not in request.files:
+        return jsonify({"error": "No image file provided"}), 400
+
+    file = request.files['imageFile']
+    if file.filename == '':
+        return jsonify({"error": "No image selected"}), 400
+
+    if file:
+        try:
+            filename = "uploaded_image_" + file.filename  # Add some uniqueness if needed
+            image_path = os.path.join(app.config['UPLOAD_FOLDER'], filename)
+            file.save(image_path)
+
+            custom_prompt_for_perfume = (
+                "Based on the visual elements, mood, and potential theme of this image, "
+                "invent a unique perfume. I need the following information structured as a JSON object:\n"
+                "1. 'perfume_name': A creative and fitting name for the perfume.\n"
+                "2. 'slogan': A short, catchy slogan for this perfume.\n"
+                "3. 'aromas': A list of 2 to 10 base aroma notes. For each note, provide its 'name' "
+                f"(chosen from this list: {', '.join(BASE_AROMA_NAMES_ORDER)}, or a very similar common scent if appropriate) "
+                "and its 'dose' as a decimal proportion (e.g., 0.3 for 30%). "
+                "Ideally, the total dose should be equal to a full bottle of 10.0 for 100%"
+                "\nExample of 'aromas' list: [{'name': 'Rose', 'dose': 0.4}, {'name': 'Lavender', 'dose': 0.2}]"
+            )
+
+            api_result_raw = client.predict(
+                files_list=[handle_file(image_path)],
+                message=custom_prompt_for_perfume,
+                model_name="gemini-2.0-flash",
+                temperature=0.7,
+                max_output_tokens=1024,
+                system_prompt_load= "",
+                type_mime_name="application/json",
+                output_schema=None,
+                google_search_opt=False,
+                other_options_json=None,
+                thinking_mode=False,
+                thinking_budget=None,
+                api_name="/predict"
+            )
+
+
+            os.remove(image_path)  # Clean up uploaded file
+
+            # print("Raw API Result:", api_result_raw)
+            api_result_raw = api_result_raw[0]["candidates"][0]["content"]["parts"][0]['text']
+            print("Raw API Result:", api_result_raw)
+            return api_result_raw
+
+        except Exception as e:
+            print(f"Error during API call or processing: {e}")
+            # In case of any error, return demo data for now for frontend testing
+            # os.remove(image_path) # Ensure cleanup even on error
+            error_data = get_demo_perfume_data()
+            error_data["api_error"] = f"An error occurred: {str(e)}. Displaying demo data."
+            return jsonify(error_data), 500  # Send 500 to indicate server-side error
+
+    return jsonify({"error": "Invalid file"}), 400
+
+
+if __name__ == '__main__':
+    app.run(debug=True)  # Use a different port if 5000 is common