Update app.py

app.py

@@ -2,105 +2,112 @@ import whisper
 import gradio as gr
 from groq import Groq
 from deep_translator import GoogleTranslator
-from PIL import Image
+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("GROQ_API_KEY")
 client = Groq(api_key=api_key)
-
+ 
 # Hugging Face API details for image generation
 H_key = os.getenv("Hugging_api_key")
 API_URL = "https://api-inference.huggingface.co/models/Artples/LAI-ImageGeneration-vSDXL-2"
 headers = {"Authorization": f"Bearer {H_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"
-    )
+                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:
             transcription = client.audio.transcriptions.create(
                 file=(os.path.basename(audio_path), file.read()),
                 model="whisper-large-v3",
-                language="kn",  # Change to Kannada
+                language="ta",
                 response_format="verbose_json",
             )
-        kannada_text = transcription.text  # Changed from tamil_text to kannada_text
+        tamil_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 Tamil to English
     try:
-        translator = GoogleTranslator(source='kn', target='en')
-        translation = translator.translate(kannada_text)  # Changed from tamil_text to kannada_text
+        translator = GoogleTranslator(source='ta', target='en')
+        translation = translator.translate(tamil_text)
     except Exception as e:
-        return kannada_text, f"An error occurred during translation: {str(e)}", None, None
-
+        return tamil_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)
-
+ 
     # 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, "An error occurred during image generation."
-
-    return kannada_text, translation, creative_text, image      
-
+            return tamil_text, translation, creative_text, f"An error occurred during image generation"
+ 
+    return tamil_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")
@@ -111,15 +118,15 @@ with gr.Blocks(theme=gr.themes.Base()) as iface:
             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")  # Changed to Kannada
+            tamil_text_output = gr.Textbox(label="Tamil 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]
+        outputs=[tamil_text_output, translation_output, creative_text_output, image_output]
     )
-
+ 
 # Launch the interface
-iface.launch()
+iface.launch()