Update app.py

app.py

@@ -1,63 +1,63 @@
 import gradio as gr
-from huggingface_hub import InferenceClient
-
-"""
-For more information on `huggingface_hub` Inference API support, please check the docs: https://huggingface.co/docs/huggingface_hub/v0.22.2/en/guides/inference
-"""
-client = InferenceClient("HuggingFaceH4/zephyr-7b-beta")
-
-
-def respond(
-    message,
-    history: list[tuple[str, str]],
-    system_message,
-    max_tokens,
-    temperature,
-    top_p,
-):
-    messages = [{"role": "system", "content": system_message}]
-
-    for val in history:
-        if val[0]:
-            messages.append({"role": "user", "content": val[0]})
-        if val[1]:
-            messages.append({"role": "assistant", "content": val[1]})
-
-    messages.append({"role": "user", "content": message})
-
-    response = ""
-
-    for message in client.chat_completion(
-        messages,
-        max_tokens=max_tokens,
-        stream=True,
-        temperature=temperature,
-        top_p=top_p,
-    ):
-        token = message.choices[0].delta.content
-
-        response += token
-        yield response
-
+import requests
+import os
+
+# Retrieve secrets
+API_URL = os.environ.get("API_URL")
+HF_TOKEN = os.environ.get("HF_TOKEN")
+
+# Hugging Face Inference API details
+headers = {
+    "Accept": "application/json",
+    "Authorization": f"Bearer {HF_TOKEN}",
+    "Content-Type": "application/json"
+}
+
+def query(payload):
+    response = requests.post(API_URL, headers=headers, json=payload)
+    return response.json()
+
+def translate(text, source_lang, target_lang):
+    if source_lang == target_lang:
+        return text
+    
+    instruction = f"Translate the text from {source_lang} to {target_lang}"
+    
+    input_text = f"""### Instruction: {instruction}
+
+### Input: {text}
+
+### Response:
 """
-For information on how to customize the ChatInterface, peruse the gradio docs: https://www.gradio.app/docs/chatinterface
-"""
-demo = gr.ChatInterface(
-    respond,
-    additional_inputs=[
-        gr.Textbox(value="You are a friendly Chatbot.", label="System message"),
-        gr.Slider(minimum=1, maximum=2048, value=512, step=1, label="Max new tokens"),
-        gr.Slider(minimum=0.1, maximum=4.0, value=0.7, step=0.1, label="Temperature"),
-        gr.Slider(
-            minimum=0.1,
-            maximum=1.0,
-            value=0.95,
-            step=0.05,
-            label="Top-p (nucleus sampling)",
-        ),
+    
+    # Generate the translation using the Hugging Face Inference API
+    output = query({
+        "inputs": input_text,
+        "parameters": {"max_length": 1024}
+    })
+    
+    # Extract the translated text from the API response
+    # Adjust this based on the actual format of your API response
+    translated_text = output[0]['generated_text'].split("### Response:")[-1].strip()
+    
+    return translated_text
+
+# Create the Gradio interface
+iface = gr.Interface(
+    fn=translate,
+    inputs=[
+        gr.Textbox(label="Enter text to translate"),
+        gr.Radio(["English", "Welsh"], label="Source Language", value="English"),
+        gr.Radio(["English", "Welsh"], label="Target Language", value="Welsh"),
+    ],
+    outputs=gr.Textbox(label="Translated Text"),
+    title="English-Welsh Translator",
+    description="Translate text between English and Welsh using a custom language model via Hugging Face Inference API.",
+    examples=[
+        ["Hello, how are you?", "English", "Welsh"],
+        ["Bore da!", "Welsh", "English"],
     ],
 )
 
-
-if __name__ == "__main__":
-    demo.launch()
+# Launch the app
+iface.launch()