Update app.py

app.py

@@ -1,45 +1,52 @@
-from flask import Flask, render_template, jsonify
+from flask import Flask, render_template, request, jsonify
+import torch
+from transformers import pipeline
+import gradio as gr
 
 app = Flask(__name__)
 
-# Replace with your AssemblyAI API key
-ASSEMBLYAI_API_KEY = "67883cd71f0d4a58a27a34e058f0d924"
-
-# URL of the file to transcribe
-FILE_URL = "/content/call.mp3"
-
-# You can also transcribe a local file by passing in a file path
-# FILE_URL = './path/to/file.mp3'
-
-@app.route('/analyze', methods=['GET'])
-def analyze():
-    # Transcribe audio to text
-    transcriber = aai.Transcriber(api_key=ASSEMBLYAI_API_KEY)
-    transcript = transcriber.transcribe(FILE_URL)
-    text = transcript.text
-
-    # Perform sentiment analysis
-    sentiment_analyzer = pipeline("sentiment-analysis")
-    sentiment = sentiment_analyzer(text)
-
-    # Perform emotion analysis
-    emotion_analyzer = pipeline("text-classification", model="j-hartmann/emotion-english-distilroberta-base", return_all_scores=True)
-    emotions = emotion_analyzer(text)
-
-    # Format results
-    result = {
-        "transcript": text,
-        "sentiment": {
-            "score": sentiment[0]['score'],
-            "label": sentiment[0]['label']
-        },
-        "emotion": [{
-            "label": emotion['label'],
-            "score": emotion['score']
-        } for emotion in emotions[0]]
-    }
-
-    return render_template('analyze.html', result=result)
+# Load the automatic speech recognition model
+pipe = pipeline("automatic-speech-recognition",
+               "openai/whisper-large-v3",
+               torch_dtype=torch.float16,
+               device="cuda:0")
+
+# Load the emotion classification model
+emotion_classifier = pipeline(
+    "text-classification",
+    model="j-hartmann/emotion-english-distilroberta-base",
+    return_all_scores=True
+)
+
+def transcribe(audio_file, task):
+    if audio_file is None:
+        return "Please upload or record an audio file."
+
+    # Check if the audio file is in bytes format (drag-and-drop file)
+    if isinstance(audio_file, bytes):
+        text = pipe(audio_file, generate_kwargs={"task": task}, return_timestamps=True)["text"]
+    else:
+        # Handle the case where the file is uploaded using the file uploader
+        text = pipe(audio_file.name, generate_kwargs={"task": task}, return_timestamps=True)["text"]
+
+    return text
+
+@app.route('/')
+def index():
+    return render_template('index.html')
+
+@app.route('/transcribe', methods=['POST'])
+def transcribe_endpoint():
+    audio_file = request.files.get('audio_file')
+    task = request.form.get('task')
+    text = transcribe(audio_file, task)
+    return jsonify({'text': text})
+
+@app.route('/classify_emotion', methods=['POST'])
+def classify_emotion_endpoint():
+    text = request.form.get('text')
+    result = emotion_classifier(text)
+    return jsonify(result)
 
 if __name__ == '__main__':
     app.run(debug=True)