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app.py

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+import gradio as gr
+import openai
+import whisper
+
+# Load the Whisper model
+model = whisper.load_model("base")
+
+# Function to process audio and generate transcription and summary
+def procesare_audio(file):
+    # Transcribe audio
+    result = model.transcribe(file.name)
+    transcriere_text = result["text"]
+
+    # Use OpenAI to summarize the transcription
+    openai.api_key = "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" 
+    response = openai.ChatCompletion.create(
+        model="gpt-3.5-turbo",
+        messages=[
+            {"role": "system", "content": "You are a helpful assistant that summarizes text."},
+            {"role": "user", "content": f"Please summarize the following text: {transcriere_text}"}
+        ]
+    )
+    rezumat_text = response.choices[0].message.content.strip()
+
+    return transcriere_text, rezumat_text
+
+# Define the Gradio interface
+interface = gr.Interface(
+    fn=procesare_audio,
+    inputs=gr.Audio(source="upload", type="file"),
+    outputs=[
+        gr.Textbox(label="Transcrierea textului:"),
+        gr.Textbox(label="Rezumatul textului:")
+    ],
+    title="Transcriere și Rezumat AI",
+    description="Această aplicație transcrie fișiere audio și creează un rezumat al conținutului folosind AI.",
+    theme="compact"
+)
+
+# Launch the Gradio app
+interface.launch(share=False, debug=True)

requirements.txt

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+gradio
+openai
+torch
+openai-whisper