create app.py

app.py

@@ -0,0 +1,69 @@
+import os
+import streamlit as st
+import requests
+import pyttsx3
+from transformers import pipeline
+from PIL import Image
+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"
+headers = {"Authorization": f"Bearer {os.getenv('HUGGINGFACE_API_KEY')}"}
+
+# Function to query the Hugging Face model for facial expression
+def query(filename):
+    with open(filename, "rb") as f:
+        data = f.read()
+    response = requests.post(API_URL, headers=headers, data=data)
+    
+    if response.status_code == 200:
+        return response.json()
+    else:
+        st.error("Error detecting facial expression: " + response.text)
+        return None
+
+# Function to generate a joke or uplifting text based on the mood
+def generate_text_based_on_mood(emotion):
+    generator = pipeline('text-generation', model='gpt2')
+    prompt = f"Tell a joke that would cheer someone who is feeling {emotion}."
+    response = generator(prompt, max_length=50, num_return_sequences=1)
+    return response[0]['generated_text']
+
+# Function to convert text to speech
+def text_to_speech(text):
+    engine = pyttsx3.init()
+    engine.say(text)
+    engine.runAndWait()
+
+# Streamlit UI
+st.title("Facial Expression Mood Detector")
+st.write("Upload an image of a face to detect mood and receive uplifting messages or jokes.")
+
+# Upload image
+uploaded_file = st.file_uploader("Choose an image...", type=["jpg", "jpeg", "png"])
+
+if uploaded_file is not None:
+    # Load and display the image
+    image = Image.open(uploaded_file)
+    st.image(image, caption='Uploaded Image', use_column_width=True)
+
+    # Save the uploaded file temporarily
+    with open("uploaded_image.jpg", "wb") as f:
+        f.write(uploaded_file.getbuffer())
+
+    # Detect facial expression
+    expression_output = query("uploaded_image.jpg")
+    if expression_output:
+        emotion = expression_output[0]['label']  # Adjust as per the actual response structure
+        st.write(f"Detected emotion: {emotion}")
+
+        # Generate text based on detected emotion
+        joke = generate_text_based_on_mood(emotion)
+        st.write("Here's something to cheer you up:")
+        st.write(joke)
+
+        # Convert the generated joke to audio
+        text_to_speech(joke)