Create app.py

app.py

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+# Load text generation model and tokenizer
+device = "cuda" if torch.cuda.is_available() else "cpu"
+text_generation_model = AutoModelForCausalLM.from_pretrained("gpt2").to(device)  # Move model to the correct device
+text_generation_tokenizer = AutoTokenizer.from_pretrained("gpt2")
+ 
+# Set the padding token
+text_generation_tokenizer.pad_token = text_generation_tokenizer.eos_token  # Use EOS token as padding token
+ 
+# Function to transcribe, translate, analyze sentiment, and generate image
+def process_audio(audio_path, image_option):
+    if audio_path is None:
+        return "Please upload an audio file.", None, None, None
+ 
+    # Step 1: Transcribe audio
+    try:
+        with open(audio_path, "rb") as file:
+            transcription = client.audio.transcriptions.create(
+                file=(os.path.basename(audio_path), file.read()),
+                model="whisper-large-v3",
+                language="ta",
+                response_format="verbose_json",
+            )
+        tamil_text = transcription.text
+    except Exception as e:
+        return f"An error occurred during transcription: {str(e)}", None, None, None
+ 
+    # Step 2: Translate Tamil to English
+    try:
+        translator = GoogleTranslator(source='ta', target='en')
+        translation = translator.translate(tamil_text)
+    except Exception as e:
+        return tamil_text, f"An error occurred during translation: {str(e)}", None, None
+ 
+    # Step 3: Generate creative text
+    def generate_creative_text(english_text):
+        if not english_text:
+            return "Please provide text to generate creative content."
+ 
+        try:
+            inputs = text_generation_tokenizer(english_text, return_tensors="pt", padding=True, truncation=True).to(device)  # Move inputs to the same device
+            generated_tokens = text_generation_model.generate(
+                **inputs,
+                max_length=60,
+                num_return_sequences=1,
+                no_repeat_ngram_size=3,
+                temperature=0.7,
+                top_p=0.9,
+                do_sample=True,
+                early_stopping=True
+            )
+            creative_text = text_generation_tokenizer.decode(generated_tokens[0], skip_special_tokens=True).strip()
+            return creative_text
+        except Exception as e:
+            return f"An error occurred during text generation: {str(e)}"
+ 
+    creative_text = generate_creative_text(translation)
+ 
+    # Step 4: Generate image (if selected)
+    image = None
+    if image_option == "Generate Image":
+        try:
+            image = pipe(translation).images[0]
+        except Exception as e:
+            return tamil_text, translation, f"An error occurred during image generation: {str(e)}", None
+ 
+    return tamil_text, translation, image, creative_text
+ 
+# Create Gradio interface
+with gr.Blocks() as iface:
+    gr.Markdown("# Audio Transcription, Translation, and Image Generation")
+    with gr.Row():
+        with gr.Column():
+            audio_input = gr.Audio(type="filepath", label="Upload Audio File")
+            image_option = gr.Dropdown(["Generate Image", "Skip Image"], label="Image Generation", value="Generate Image")
+            submit_button = gr.Button("Process Audio")
+        with gr.Column():
+            tamil_text_output = gr.Textbox(label="Tamil Transcription", interactive=False)
+            translation_output = gr.Textbox(label="English Translation", interactive=False)
+            image_output = gr.Image(label="Generated Image")
+            creative_text_output = gr.Textbox(label="Creative Text", interactive=False)
+ 
+    submit_button.click(
+        fn=process_audio,
+        inputs=[audio_input, image_option],
+        outputs=[tamil_text_output, translation_output, image_output, creative_text_output]
+    )
+ 
+# Launch the interface
+iface.launch()