Create app.py

app.py

@@ -0,0 +1,45 @@
+from flask import Flask, render_template, jsonify
+
+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)
+
+if __name__ == '__main__':
+    app.run(debug=True)