Update app.py

app.py

@@ -1,79 +1,7 @@
-import whisper
-import gradio as gr
-from groq import Groq
-from deep_translator import GoogleTranslator
-from diffusers import StableDiffusionPipeline
-import os
-import torch
-import openai
-from huggingface_hub import InferenceApi
-from PIL import Image
-import requests
-import io
-import time
- 
-# Set up Groq API key
-api_key = os.getenv("gkannada_key")
-client = Groq(api_key=api_key)
- 
-# Hugging Face API details for image generation
-key = os.getenv("h_key")
-API_URL = "https://api-inference.huggingface.co/models/Artples/LAI-ImageGeneration-vSDXL-2"
-headers = {"Authorization": f"Bearer {key}"}
- 
- 
-# Function for querying image generation with retries
-def query_image_generation(payload, max_retries=5):
-    for attempt in range(max_retries):
-        response = requests.post(API_URL, headers=headers, json=payload)
- 
-        if response.status_code == 503:
-            print(f"Model is still loading, retrying... Attempt {attempt + 1}/{max_retries}")
-            estimated_time = min(response.json().get("estimated_time", 60), 60)
-            time.sleep(estimated_time)
-            continue
- 
-        if response.status_code != 200:
-            print(f"Error: Received status code {response.status_code}")
-            print(f"Response: {response.text}")
-            return None
- 
-        return response.content
- 
-    print(f"Failed to generate image after {max_retries} attempts.")
-    return None
- 
-# Function for generating an image from text
-def generate_image(prompt):
-    image_bytes = query_image_generation({"inputs": prompt})
- 
-    if image_bytes is None:
-        return None
- 
-    try:
-        image = Image.open(io.BytesIO(image_bytes))  # Opening the image from bytes
-        return image
-    except Exception as e:
-        print(f"Error: {e}")
-        return None
- 
- 
-# Updated function for text generation using the new API structure
-def generate_creative_text(prompt):
-    chat_completion = client.chat.completions.create(
-                messages=[
-                    {"role": "user", "content":prompt}
-                ],
-                model="llama-3.2-90b-text-preview"
-            )
-    chatbot_response = chat_completion.choices[0].message.content
-    return chatbot_response
- 
- 
 def process_audio(audio_path, image_option, creative_text_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:
@@ -83,50 +11,30 @@ def process_audio(audio_path, image_option, creative_text_option):
                 language="kn",
                 response_format="verbose_json",
             )
-        tamil_text = transcription.text
+        kannada_text = transcription.text
     except Exception as e:
         return f"An error occurred during transcription: {str(e)}", None, None, None
-    # Step 2: Translate kannada to English
+
+    # Step 2: Translate Kannada to English
     try:
         translator = GoogleTranslator(source='kn', target='en')
         translation = translator.translate(kannada_text)
     except Exception as e:
         return kannada_text, f"An error occurred during translation: {str(e)}", None, None
- 
+
     # Step 3: Generate creative text (if selected)
     creative_text = None
     if creative_text_option == "Generate Creative Text":
-        creative_text = generate_creative_text(translation)
- 
+        try:
+            creative_text = generate_creative_text(translation)
+        except Exception as e:
+            creative_text = f"An error occurred during creative text generation: {str(e)}"
+
     # Step 4: Generate image (if selected)
     image = None
     if image_option == "Generate Image":
         image = generate_image(translation)
         if image is None:
-            return kannada_text, translation, creative_text, f"An error occurred during image generation"
- 
+            return kannada_text, translation, creative_text, "An error occurred during image generation."
+
     return kannada_text, translation, creative_text, image      
- 
- 
-# Create Gradio interface
-with gr.Blocks(theme=gr.themes.Base()) as iface:
-    gr.Markdown("# Audio Transcription, Translation, Image & Creative Text 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")
-            creative_text_option = gr.Dropdown(["Generate Creative Text", "Skip Creative Text"], label="Creative Text Generation", value="Generate Creative Text")
-            submit_button = gr.Button("Process Audio")
-        with gr.Column():
-            kannada_text_output = gr.Textbox(label="kannada Transcription")
-            translation_output = gr.Textbox(label="English Translation")
-            creative_text_output = gr.Textbox(label="Creative Text")
-            image_output = gr.Image(label="Generated Image")
-    submit_button.click(
-        fn=process_audio,
-        inputs=[audio_input, image_option, creative_text_option],
-        outputs=[kannada_text_output, translation_output, creative_text_output, image_output]
-    )
- 
-# Launch the interface
-iface.launch()