Update app.py

app.py

@@ -1,154 +1,149 @@
+import os
 import gradio as gr
-import numpy as np
-import random
-
-# import spaces #[uncomment to use ZeroGPU]
-from diffusers import DiffusionPipeline
-import torch
-
-device = "cuda" if torch.cuda.is_available() else "cpu"
-model_repo_id = "stabilityai/sdxl-turbo"  # Replace to the model you would like to use
-
-if torch.cuda.is_available():
-    torch_dtype = torch.float16
-else:
-    torch_dtype = torch.float32
-
-pipe = DiffusionPipeline.from_pretrained(model_repo_id, torch_dtype=torch_dtype)
-pipe = pipe.to(device)
-
-MAX_SEED = np.iinfo(np.int32).max
-MAX_IMAGE_SIZE = 1024
-
-
-# @spaces.GPU #[uncomment to use ZeroGPU]
-def infer(
-    prompt,
-    negative_prompt,
-    seed,
-    randomize_seed,
-    width,
-    height,
-    guidance_scale,
-    num_inference_steps,
-    progress=gr.Progress(track_tqdm=True),
-):
-    if randomize_seed:
-        seed = random.randint(0, MAX_SEED)
-
-    generator = torch.Generator().manual_seed(seed)
-
-    image = pipe(
-        prompt=prompt,
-        negative_prompt=negative_prompt,
-        guidance_scale=guidance_scale,
-        num_inference_steps=num_inference_steps,
-        width=width,
-        height=height,
-        generator=generator,
-    ).images[0]
-
-    return image, seed
-
-
-examples = [
-    "Astronaut in a jungle, cold color palette, muted colors, detailed, 8k",
-    "An astronaut riding a green horse",
-    "A delicious ceviche cheesecake slice",
-]
-
-css = """
-#col-container {
-    margin: 0 auto;
-    max-width: 640px;
-}
-"""
-
-with gr.Blocks(css=css) as demo:
-    with gr.Column(elem_id="col-container"):
-        gr.Markdown(" # Text-to-Image Gradio Template")
-
-        with gr.Row():
-            prompt = gr.Text(
-                label="Prompt",
-                show_label=False,
-                max_lines=1,
-                placeholder="Enter your prompt",
-                container=False,
+import requests
+import io
+import re
+from PIL import Image
+from groq import Groq
+
+# Set Your API Keys
+#  Use environment variables securely
+GROQ_API_KEY = os.getenv("GROQ_API_KEY")
+HF_API_KEY = os.getenv("HF_TOKEN")
+
+if not GROQ_API_KEY or not HF_API_KEY:
+    raise ValueError("GROQ_API_KEY and HF_TOKEN must be set in the environment variables.")
+# Initialize Groq API client
+client = Groq(api_key=GROQ_API_KEY)
+
+# Use a Public Hugging Face Image Model
+HF_IMAGE_MODEL = "stabilityai/stable-diffusion-2-1"
+
+
+# Function 1: Tamil Audio to Tamil Text (Transcription)
+def transcribe_audio(audio_path):
+    if not audio_path:
+        return "Error: Please upload an audio file."
+
+    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",  # Tamil
+                response_format="verbose_json",
             )
+        return transcription.text.strip()
 
-            run_button = gr.Button("Run", scale=0, variant="primary")
+    except Exception as e:
+        return f"Error in transcription: {str(e)}"
 
-        result = gr.Image(label="Result", show_label=False)
 
-        with gr.Accordion("Advanced Settings", open=False):
-            negative_prompt = gr.Text(
-                label="Negative prompt",
-                max_lines=1,
-                placeholder="Enter a negative prompt",
-                visible=False,
-            )
+# Function 2: Tamil Text to English Translation
+def translate_tamil_to_english(tamil_text):
+    if not tamil_text:
+        return "Error: Please enter Tamil text for translation."
 
-            seed = gr.Slider(
-                label="Seed",
-                minimum=0,
-                maximum=MAX_SEED,
-                step=1,
-                value=0,
-            )
+    prompt = f"Translate this Tamil text to English: {tamil_text}\nGive only the translated text as output."
+
+    try:
+        response = client.chat.completions.create(
+            model="llama3-8b-8192",  # Groq-supported model
+            messages=[{"role": "user", "content": prompt}],
+        )
+        translated_text = response.choices[0].message.content.strip()
+
+        #  Fix: Remove unwanted XML tags like <think></think>
+        translated_text = re.sub(r"</?think>", "", translated_text).strip()
+        return translated_text
+
+    except Exception as e:
+        return f"Error in translation: {str(e)}"
+
+
+# Function 3: English Text to Image Generation (Hugging Face)
+def generate_image(english_text):
+    if not english_text:
+        return "Error: Please enter a description for image generation."
+
+    try:
+        headers = {"Authorization": f"Bearer {HF_API_KEY}"}
+        payload = {"inputs": english_text}
+
+        response = requests.post(f"https://api-inference.huggingface.co/models/{HF_IMAGE_MODEL}",
+                                 headers=headers, json=payload)
+        response.raise_for_status()
+        image_bytes = response.content
+
+        #  Check if the response is a valid image
+        if not image_bytes:
+            return "Error: Received empty response from API."
+
+        return Image.open(io.BytesIO(image_bytes))
+
+    except Exception as e:
+        return f"Error in image generation: {str(e)}"
+
+
+# Function 4: English Text to AI-Generated Text
+
+def generate_text(english_text):
+    if not english_text:
+        return "Please enter a prompt."
+
+    try:
+        response = client.chat.completions.create(
+            model="llama3-8b-8192",  #  Ensure you're using a valid model
+            messages=[{"role": "user", "content": english_text}],
+        )
+
+        # Extract the response content
+        generated_text = response.choices[0].message.content.strip()
+
+        # Remove unwanted XML-like tags
+        cleaned_text = re.sub(r"</?think>", "", generated_text).strip()
+
+        return cleaned_text
+
+    except Exception as e:
+        return f"Error in text generation: {str(e)}"
+
+
+# Combined Function to Process All Steps
+def process_audio(audio_path):
+    # Step 1: Tamil Audio → Tamil Text
+    tamil_text = transcribe_audio(audio_path)
+    if "Error" in tamil_text:
+        return tamil_text, None, None, None
+
+    # Step 2: Tamil Text → English Text
+    english_text = translate_tamil_to_english(tamil_text)
+    if "Error" in english_text:
+        return tamil_text, english_text, None, None
+
+    # Step 3: English Text → Image
+    image = generate_image(english_text)
+    if isinstance(image, str) and "Error" in image:
+        return tamil_text, english_text, None, None
+
+    # Step 4: English Text → AI-Generated Text
+    generated_text = generate_text(english_text)
+    return tamil_text, english_text, image, generated_text
+
+
+# Create Gradio Interface
+iface = gr.Interface(
+    fn=process_audio,
+    inputs=gr.Audio(type="filepath", label="Upload Tamil Audio"),
+    outputs=[
+        gr.Textbox(label="Transcribed Tamil Text"),
+        gr.Textbox(label="Translated English Text"),
+        gr.Image(label="Generated Image"),
+        gr.Textbox(label="Generated Text from English Prompt"),
+    ],
+    title="Tamil Audio to AI Processing Pipeline",
+    description="Upload a Tamil audio file and get transcription, translation, image generation, and further text generation.",
+)
 
-            randomize_seed = gr.Checkbox(label="Randomize seed", value=True)
-
-            with gr.Row():
-                width = gr.Slider(
-                    label="Width",
-                    minimum=256,
-                    maximum=MAX_IMAGE_SIZE,
-                    step=32,
-                    value=1024,  # Replace with defaults that work for your model
-                )
-
-                height = gr.Slider(
-                    label="Height",
-                    minimum=256,
-                    maximum=MAX_IMAGE_SIZE,
-                    step=32,
-                    value=1024,  # Replace with defaults that work for your model
-                )
-
-            with gr.Row():
-                guidance_scale = gr.Slider(
-                    label="Guidance scale",
-                    minimum=0.0,
-                    maximum=10.0,
-                    step=0.1,
-                    value=0.0,  # Replace with defaults that work for your model
-                )
-
-                num_inference_steps = gr.Slider(
-                    label="Number of inference steps",
-                    minimum=1,
-                    maximum=50,
-                    step=1,
-                    value=2,  # Replace with defaults that work for your model
-                )
-
-        gr.Examples(examples=examples, inputs=[prompt])
-    gr.on(
-        triggers=[run_button.click, prompt.submit],
-        fn=infer,
-        inputs=[
-            prompt,
-            negative_prompt,
-            seed,
-            randomize_seed,
-            width,
-            height,
-            guidance_scale,
-            num_inference_steps,
-        ],
-        outputs=[result, seed],
-    )
-
-if __name__ == "__main__":
-    demo.launch()
+# Launch Gradio App
+iface.launch()