Update app.py

app.py

@@ -13,9 +13,14 @@ image_to_text = pipeline("image-to-text", model="Salesforce/blip-image-captionin
 def generate_recipe(description):
     url = "https://api-inference.huggingface.co/models/Qwen/Qwen2.5-72B-Instruct"
     headers = {"Authorization": f"Bearer {hf_token}"}
+    # Reformulamos el prompt para que sea más específico
+    prompt = (
+        f"Provide a full recipe for a dish described as: {description}. "
+        "Include a list of ingredients and detailed step-by-step instructions."
+    )
     payload = {
-        "inputs": f"Give me a list of ingredients and a step-by-step recipe for a dish described as: {description}",
-        "parameters": {"max_length": 4000}  # Increase max_length for a more detailed response
+        "inputs": prompt,
+        "parameters": {"max_length": 5000}  # Increase max_length for a more detailed response
     }
 
     response = requests.post(url, headers=headers, json=payload)
@@ -26,7 +31,7 @@ def generate_recipe(description):
         return "Error generating recipe: " + response_data["error"]
     
     # Return only the generated text, without the input prompt
-    return response_data[0]["generated_text"].replace(payload["inputs"], "").strip() if response_data else "No recipe generated."
+    return response_data[0]["generated_text"].replace(prompt, "").strip() if response_data else "No recipe generated."
 
 # Paso 3: Define la función principal para procesar la imagen y generar la receta
 def process_image(image):