Create app.py

app.py

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+import gradio as gr
+import os
+from deep_translator import GoogleTranslator
+from PIL import Image
+import requests
+import io
+import time
+
+# Replace with your actual Hugging Face API details
+os.environ['hugging']
+H_key = os.getenv('hugging')
+API_URL = "https://api-inference.huggingface.co/models/Artples/LAI-ImageGeneration-vSDXL-2"
+headers = {"Authorization": f"Bearer {H_key}"}
+
+os.environ['groq']
+api_key = os.getenv('groq')
+client = Groq(api_key=api_key)
+
+
+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
+
+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
+
+def process_audio_or_text(input_text, audio_path, generate_image_flag):
+    tamil_text, translation, image = None, None, None
+
+    if audio_path:  # Prefer audio input
+        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
+
+        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
+
+    elif input_text:  # No audio input, so use text input
+        translation = input_text
+
+
+    # Generate chatbot response
+    try:
+        chat_completion = client.chat.completions.create(
+                messages=[{"role": "user", "content": translation}],
+                model="llama-3.2-90b-text-preview"
+            )
+        chatbot_response = chat_completion.choices[0].message.content
+    except Exception as e:
+        return None, f"An error occurred during chatbot interaction: {str(e)}", None
+
+    if generate_image_flag:  # Generate image if the checkbox is checked
+        image = generate_image(translation)
+    
+    return tamil_text, chatbot_response, image  # Return both chatbot response and image (if generated)
+
+with gr.Blocks() as iface:
+    gr.Markdown("# AI Chatbot and Image Generation App")
+
+    with gr.Row():
+        with gr.Column(scale=1):  # Left side (Inputs and Buttons)
+            user_input = gr.Textbox(label="Enter Tamil text", placeholder="Type your message here...")
+            audio_input = gr.Audio(type="file path", label=" Or upload audio (for Image Generation)")
+            image_generation_checkbox = gr.Checkbox(label="Generate Image", value=False)
+
+            # Buttons
+            submit_btn = gr.Button("Submit")
+            clear_btn = gr.Button("Clear")
+
+        with gr.Column(scale=1):  # Right side (Outputs)
+            text_output_1 = gr.Textbox(label="Tamil Transcription / Chatbot Response", interactive=False)
+            text_output_2 = gr.Textbox(label="English Translation", interactive=False)
+            image_output = gr.Image(label="Generated Image")
+
+    # Connect the buttons to the functions
+    submit_btn.click(fn=process_audio_or_text,
+                     inputs=[user_input, audio_input, image_generation_checkbox],
+                     outputs=[text_output_1, text_output_2, image_output])
+    
+    clear_btn.click(lambda: ("", None, False, "", "", None),
+                    inputs=[],
+                    outputs=[user_input, audio_input, image_generation_checkbox, text_output_1, text_output_2, image_output])
+
+iface.launch()