update

app.py

@@ -8,15 +8,16 @@ from dotenv import load_dotenv
 # Load environment variables from .env file
 load_dotenv()
 
-# Set up the Hugging Face API URL and your API key
-API_URL = "https://api-inference.huggingface.co/models/trpakov/vit-face-expression"
+# Set up the Hugging Face API URLs and your API key
+emotion_model_url = "https://api-inference.huggingface.co/models/trpakov/vit-face-expression"
+text_model_url = "https://api-inference.huggingface.co/models/mrm8488/t5-base-finetuned-emotion"
 headers = {"Authorization": f"Bearer {os.getenv('HUGGINGFACE_API_KEY')}"}
 
-# Function to query the Hugging Face model for facial expression
-def query(filename):
+# Function to query the facial expression recognition model
+def query_emotion(filename):
     with open(filename, "rb") as f:
         data = f.read()
-    response = requests.post(API_URL, headers=headers, data=data)
+    response = requests.post(emotion_model_url, headers=headers, data=data)
     
     if response.status_code == 200:
         return response.json()
@@ -24,41 +25,17 @@ def query(filename):
         st.error("Error detecting facial expression: " + response.text)
         return None
 
-# Function to generate a joke or uplifting text based on the mood
+# Function to generate a joke or uplifting text based on the detected emotion
 def generate_text_based_on_mood(emotion):
     try:
-        # Predefined jokes for different emotions
-        jokes = {
-            "happy": [
-                "Why did the scarecrow win an award? Because he was outstanding in his field!",
-                "What do you call fake spaghetti? An impasta!"
-            ],
-            "sad": [
-                "Why don’t scientists trust atoms? Because they make up everything!",
-                "Why did the bicycle fall over? Because it was two-tired."
-            ],
-            "angry": [
-                "Why did the tomato turn red? Because it saw the salad dressing!",
-                "What did one angry man say to the other? Nothing, he just threw a rock!"
-            ],
-            "surprised": [
-                "Did you hear about the claustrophobic astronaut? He just needed a little space!",
-                "Why don’t skeletons fight each other? They don’t have the guts."
-            ],
-            "neutral": [
-                "What do you call cheese that isn't yours? Nacho cheese!"
-            ]
-        }
-
-        # Use predefined jokes if available
-        if emotion in jokes:
-            return jokes[emotion][0]  # Return the first joke for simplicity
-
-        # If no predefined joke, use GPT-Neo
-        generator = pipeline('text-generation', model='EleutherAI/gpt-neo-125M')
-        prompt = f"Generate a light-hearted joke for someone who is feeling {emotion}."
-        response = generator(prompt, max_length=50, num_return_sequences=1)
-        return response[0]['generated_text']
+        prompt = f"Generate a light-hearted joke or uplifting message for someone who is feeling {emotion}."
+        response = requests.post(text_model_url, headers=headers, json={"inputs": prompt})
+        
+        if response.status_code == 200:
+            return response.json()[0]['generated_text']
+        else:
+            st.error("Error generating text: " + response.text)
+            return "Sorry, I couldn't come up with a joke at this moment."
         
     except Exception as e:
         st.error(f"Error generating text: {e}")
@@ -89,7 +66,7 @@ if uploaded_file is not None:
         f.write(uploaded_file.getbuffer())
 
     # Detect facial expression
-    expression_output = query("uploaded_image.jpg")
+    expression_output = query_emotion("uploaded_image.jpg")
     if expression_output:
         # Assuming the response has a 'label' field with the detected emotion
         emotion = expression_output[0]['label']  # Adjust based on response structure