Update app.py

app.py

@@ -1,67 +1,64 @@
 import gradio as gr
-from transformers import pipeline
+from textblob import TextBlob
 from deepface import DeepFace
 import cv2
-import numpy as np
-import tempfile
 import moviepy.editor as mp
+import tempfile
+import os
 
-# Load Text Sentiment Model
-sentiment_pipeline = pipeline("sentiment-analysis")
-
-# 1. Text Sentiment Analysis
+# Sentiment analysis for text
 def analyze_text(text):
-    result = sentiment_pipeline(text)[0]
-    return f"{result['label']} ({result['score']*100:.2f}%)"
+    blob = TextBlob(text)
+    sentiment = blob.sentiment.polarity
+    emotion = "Positive" if sentiment > 0 else "Negative" if sentiment < 0 else "Neutral"
+    return f"Sentiment: {emotion} (Score: {sentiment:.2f})"
 
-# 2. Face Emotion Detection
+# Emotion detection for image
 def analyze_face(image):
     try:
-        analysis = DeepFace.analyze(image, actions=['emotion'], enforce_detection=False)
-        emotion = analysis[0]['dominant_emotion']
-        return f"Detected Emotion: {emotion}"
+        result = DeepFace.analyze(image, actions=['emotion'], enforce_detection=False)
+        dominant_emotion = result[0]['dominant_emotion']
+        return f"Dominant Emotion: {dominant_emotion}"
     except Exception as e:
-        return f"Error: {str(e)}"
-
-# 3. Video Emotion Detection
-def analyze_video(video_file):
-    temp_video_path = tempfile.NamedTemporaryFile(delete=False, suffix=".mp4").name
-    with open(temp_video_path, "wb") as f:
-        f.write(video_file.read())
-
-    clip = mp.VideoFileClip(temp_video_path)
-    frame = clip.get_frame(clip.duration / 2)  # Take middle frame
-    frame_rgb = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
+        return f"Error analyzing face: {str(e)}"
 
+# Analyze emotion in video
+def analyze_video(video_path):
     try:
-        analysis = DeepFace.analyze(frame_rgb, actions=['emotion'], enforce_detection=False)
-        emotion = analysis[0]['dominant_emotion']
-        return f"Detected Emotion in Video: {emotion}"
+        temp_folder = tempfile.mkdtemp()
+        clip = mp.VideoFileClip(video_path)
+        frame = clip.get_frame(clip.duration / 2)  # middle frame
+        frame_path = os.path.join(temp_folder, "frame.jpg")
+        cv2.imwrite(frame_path, cv2.cvtColor(frame, cv2.COLOR_RGB2BGR))
+        result = DeepFace.analyze(frame_path, actions=['emotion'], enforce_detection=False)
+        dominant_emotion = result[0]['dominant_emotion']
+        return f"Dominant Emotion in Video: {dominant_emotion}"
     except Exception as e:
-        return f"Error: {str(e)}"
+        return f"Error analyzing video: {str(e)}"
 
-# Gradio Interface
+# Create Gradio Interface
 with gr.Blocks() as demo:
-    gr.Markdown("# 🎯 Deep Learning Sentiment & Emotion Analyzer")
-    gr.Markdown("Analyze **Text**, **Face Image**, or **Video**!")
-
-    with gr.Tabs():
-        with gr.TabItem("Text Sentiment"):
-            text_input = gr.Textbox(label="Enter Text")
-            text_output = gr.Label()
-            text_button = gr.Button("Analyze Text")
-            text_button.click(analyze_text, inputs=text_input, outputs=text_output)
-
-        with gr.TabItem("Face Emotion (Image)"):
-            image_input = gr.Image(type="numpy", label="Upload Face Image")
-            image_output = gr.Label()
-            image_button = gr.Button("Analyze Face Emotion")
-            image_button.click(analyze_face, inputs=image_input, outputs=image_output)
-
-        with gr.TabItem("Video Emotion"):
-            video_input = gr.File(label="Upload Video (.mp4)")
-            video_output = gr.Label()
-            video_button = gr.Button("Analyze Video Emotion")
-            video_button.click(analyze_video, inputs=video_input, outputs=video_output)
+    gr.Markdown("# 🧠 Emotion Decoder - Sentiment & Emotion Analysis")
+    
+    with gr.Tab("Text Analysis"):
+        text_input = gr.Textbox(label="Enter text")
+        text_output = gr.Textbox(label="Sentiment Result")
+        text_button = gr.Button("Analyze Text")
+        text_button.click(analyze_text, inputs=text_input, outputs=text_output)
+    
+    with gr.Tab("Face Emotion Detection"):
+        img_input = gr.Image(type="filepath", label="Upload an Image")
+        img_output = gr.Textbox(label="Emotion Result")
+        img_button = gr.Button("Analyze Face Emotion")
+        img_button.click(analyze_face, inputs=img_input, outputs=img_output)
+    
+    with gr.Tab("Video Emotion Detection"):
+        video_input = gr.Video(label="Upload a Video")
+        video_output = gr.Textbox(label="Emotion Result")
+        video_button = gr.Button("Analyze Video Emotion")
+        video_button.click(analyze_video, inputs=video_input, outputs=video_output)
 
 demo.launch()
+
+   
+