Create app.py

app.py

@@ -0,0 +1,61 @@
+from flask import Flask, request, render_template
+import google.generativeai as genai
+import os
+from PIL import Image
+import io
+
+app = Flask(__name__)
+
+# Configuration de l'API 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,
+)
+
+
+@app.route("/", methods=["GET", "POST"])
+def index():
+    e = ""
+    if request.method == "POST":
+        if "image" not in request.files:
+            e = "Aucune image sélectionnée."
+        else:
+            image_file = request.files["image"]
+            try:
+                image = Image.open(io.BytesIO(image_file.read()))
+
+                response = model.generate_content([mm, image])  # Passage de l'image
+                print(response.text)
+                e = response.text
+
+
+            except Exception as e: # gérer les erreurs potentielles d'ouverture de l'image
+                  e = f"Erreur lors du traitement de l'image : {str(e)}"
+
+
+    return render_template("index.html", e=e)
+
+
+if __name__ == "__main__":
+    app.run(debug=True)