Create app.py

app.py

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+import streamlit as st
+from transformers import pipeline
+import torch
+from scipy.io.wavfile import write
+import numpy as np
+
+# Load Hugging Face pipelines
+emotion_classifier = pipeline("text-classification", model="bhadresh-savani/bert-base-go-emotion")
+feedback_generator = pipeline("text2text-generation", model="mrm8488/t5-small-finetuned-emotion")
+text_to_audio = pipeline("text-to-speech", model="facebook/mms-tts-eng")
+
+# Streamlit app UI
+st.title("Emotion Detection and Feedback Generation")
+st.markdown("""
+This app detects emotions in a given comment, generates appropriate feedback, and reads it aloud.
+""")
+
+# Input text box for comments
+comment_input = st.text_area("Enter your comment:", placeholder="Type your comment here...", height=200)
+
+# Analyze button
+if st.button("Analyze Comment"):
+    if not comment_input.strip():
+        st.error("Please provide a valid comment.")
+    else:
+        # Perform emotion classification
+        emotion_result = emotion_classifier(comment_input)[0]
+        emotion_label = emotion_result["label"]
+        emotion_score = round(emotion_result["score"], 4)
+
+        # Generate feedback based on emotion
+        feedback = feedback_generator(f"emotion: {emotion_label} text: {comment_input}", max_length=50)[0]["generated_text"]
+
+        # Convert feedback text to speech
+        audio_output = text_to_audio(feedback)
+        audio_array = np.array(audio_output["audio"]) * 32767  # Convert to int16 range
+        audio_path = "feedback_audio.wav"
+        write(audio_path, 22050, audio_array.astype(np.int16))
+        
+        # Display results
+        st.subheader("Analysis Result")
+        st.write(f"### **Emotion:** {emotion_label} (Confidence: {emotion_score})")
+        st.write(f"### **Generated Feedback:** {feedback}")
+        
+        # Audio output
+        st.audio(audio_path, format="audio/wav")