Update app.py

app.py

@@ -1,117 +1,62 @@
-from flask import Flask, render_template, request, send_file
+from flask import Flask, render_template, request, jsonify
 import google.generativeai as genai
 import os
-import re
-import subprocess
-import uuid
-import tempfile
+from PIL import Image
+import io
+import base64
 
-# Configuration de l'API Gemini
+app = Flask(__name__)
+
+# Configuration Gemini
 token = os.environ.get("TOKEN")
 genai.configure(api_key=token)
+
 generation_config = {
     "temperature": 1,
     "top_p": 0.95,
     "top_k": 64,
     "max_output_tokens": 8192,
 }
+
 safety_settings = [
     {"category": "HARM_CATEGORY_HARASSMENT", "threshold": "BLOCK_NONE"},
     {"category": "HARM_CATEGORY_HATE_SPEECH", "threshold": "BLOCK_NONE"},
     {"category": "HARM_CATEGORY_SEXUALLY_EXPLICIT", "threshold": "BLOCK_NONE"},
     {"category": "HARM_CATEGORY_DANGEROUS_CONTENT", "threshold": "BLOCK_NONE"},
 ]
+
+mm = """ resous cet exercice. tu répondras en détaillant au maximum ton procédé de calcul. réponse attendue uniquement en Latex"""
+
 model = genai.GenerativeModel(
     model_name="gemini-1.5-pro",
     generation_config=generation_config,
-    safety_settings=safety_settings,
+    safety_settings=safety_settings
 )
 
-app = Flask(__name__)
-
-# Dossiers pour les fichiers temporaires
-app.config['UPLOAD_FOLDER'] = 'static/temp_images'
-app.config['CHEM_RENDERS_FOLDER'] = 'static/chem_renders'
-if not os.path.exists(app.config['UPLOAD_FOLDER']):
-    os.makedirs(app.config['UPLOAD_FOLDER'])
-if not os.path.exists(app.config['CHEM_RENDERS_FOLDER']):
-    os.makedirs(app.config['CHEM_RENDERS_FOLDER'])
-
-# Fonction pour extraire et traiter les structures chimiques
-def process_chemfig(text):
-    chemfig_pattern = r"\\chemfig{(.*?)}"
-    matches = re.findall(chemfig_pattern, text)
-    image_paths = []
-
-    for match in matches:
-        try:
-            # Créer un fichier LaTeX temporaire
-            tex_filename = str(uuid.uuid4()) + '.tex'
-            tex_filepath = os.path.join(app.config['CHEM_RENDERS_FOLDER'], tex_filename)
-            with open(tex_filepath, 'w') as tex_file:
-                tex_file.write(r'\documentclass[margin=10pt]{standalone}\usepackage{chemfig}\begin{document}\chemfig{' + match + r'}\end{document}')
-
-            # Compiler le fichier LaTeX en PDF
-            pdf_filename = tex_filename[:-4] + '.pdf'
-            pdf_filepath = os.path.join(app.config['CHEM_RENDERS_FOLDER'], pdf_filename)
-            subprocess.run(['pdflatex', '-output-directory', app.config['CHEM_RENDERS_FOLDER'], tex_filepath], check=True)
-
-            # Convertir le PDF en SVG
-            svg_filename = tex_filename[:-4] + '.svg'
-            svg_filepath = os.path.join(app.config['CHEM_RENDERS_FOLDER'], svg_filename)
-            subprocess.run(['pdf2svg', pdf_filepath, svg_filepath], check=True)
-
-            # Remplacer le motif chemfig par la balise img
-            image_path = f'/chem_renders/{svg_filename}'
-            image_paths.append(image_path)
-            text = text.replace(f"\\chemfig{{{match}}}", f'<img src="{image_path}" style="max-width:100%; height:auto;" alt="Structure Chimique">')
-
-            # Supprimer les fichiers temporaires
-            os.remove(tex_filepath)
-            os.remove(pdf_filepath)
-
-        except Exception as e:
-            text = text.replace(f"\\chemfig{{{match}}}", f'Erreur lors du rendu de la structure : {e}')
-
-    return text, image_paths
-
-# Route principale
-@app.route('/', methods=['GET', 'POST'])
-def index():
-    generated_content = ""
-    image_paths = []
-    if request.method == 'POST':
-        image_file = request.files.get('image')
-        mm = """ resous cet exercice. tu répondras en détaillant au maximum ton procédé de calcul. réponse attendue uniquement en Latex 
-
-"""
-        if image_file :
-            image_bytes = image_file.read()
-            parts=[
-                 {
-                        "mime_type":"image/jpeg",
-                        "data": image_bytes
-                        },
-                mm
-            ]
-            response = model.generate_content(parts)
-            generated_content = response.text
-        else:
-            text_input= request.form.get('text_input')
-            response = model.generate_content(mm+text_input)
-            generated_content = response.text
-
-        generated_content, image_paths = process_chemfig(generated_content)
-    return render_template('index.html', generated_content=generated_content, image_paths=image_paths)
-
-# Routes pour servir les fichiers temporaires
-@app.route('/temp_images/<filename>')
-def temp_image(filename):
-    return send_file(os.path.join(app.config['UPLOAD_FOLDER'], filename), mimetype='image/png')
-
-@app.route('/chem_renders/<filename>')
-def chem_render(filename):
-    return send_file(os.path.join(app.config['CHEM_RENDERS_FOLDER'], filename), mimetype='image/svg+xml')
+@app.route('/')
+def home():
+    return render_template('index.html')
+
+@app.route('/generate', methods=['POST'])
+def generate():
+    try:
+        # Récupérer l'image depuis la requête
+        image_data = request.json.get('image')
+        if not image_data:
+            return jsonify({"result": "djo"})
+        
+        # Convertir l'image base64 en image PIL
+        image_data = image_data.split(',')[1]
+        image_bytes = base64.b64decode(image_data)
+        image = Image.open(io.BytesIO(image_bytes))
+        
+        # Générer le contenu
+        response = model.generate_content([mm, image])
+        result = response.text
+        
+        return jsonify({"result": result})
+    except Exception as e:
+        return jsonify({"error": str(e)}), 500
 
 if __name__ == '__main__':
     app.run(debug=True)