Update app.py

app.py

@@ -1,85 +1,27 @@
-# app.py
+# test_groq_api.py
 
-import gradio as gr
-from transformers import AutoTokenizer
-from groq import Groq
 import os
-from huggingface_hub import login
+from groq import Groq
 
 # Initialize Groq API client
-try:
-    client = Groq(api_key=os.environ["GROQ_API_KEY"])
-except KeyError:
-    raise ValueError("GROQ_API_KEY environment variable not set.")
-
-# Load the Hugging Face token from the environment variable
-hf_token = os.getenv('HF_TOKEN')
-if hf_token is None:
-    raise ValueError("Hugging Face token not found. Please set it as an environment variable.")
+client = Groq(api_key=os.environ["GROQ_API_KEY"])
 
-# Authentication token for Hugging Face
-login(token=hf_token)
-
-# Model identifier for Groq API (you can replace it with your HF model if needed)
+# Model identifier for Groq API
 model_name = "asthaa30/l3.1"
 
-# Load tokenizer (model will be accessed via Groq API)
+messages = [
+    {"role": "system", "content": "You are a friendly Chatbot."},
+    {"role": "user", "content": "Hello!"},
+]
+
 try:
-    tokenizer = AutoTokenizer.from_pretrained(model_name, token=hf_token)
+    response = client.chat.completions.create(
+        model=model_name,
+        messages=messages,
+        max_tokens=512,
+        temperature=0.7,
+        top_p=0.95,
+    )
+    print(response)
 except Exception as e:
-    raise ValueError(f"Failed to load tokenizer: {e}")
-
-def respond(
-    message: str,
-    history: list[tuple[str, str]],
-    system_message: str,
-    max_tokens: int,
-    temperature: float,
-    top_p: float,
-) -> str:
-    messages = [{"role": "system", "content": system_message}]
-
-    for user_msg, assistant_msg in history:
-        if user_msg:
-            messages.append({"role": "user", "content": user_msg})
-        if assistant_msg:
-            messages.append({"role": "assistant", "content": assistant_msg})
-
-    messages.append({"role": "user", "content": message})
-
-    # Use Groq API to get the model's response
-    try:
-        response = client.chat.completions.create(
-            model=model_name,
-            messages=messages,
-            max_tokens=max_tokens,
-            temperature=temperature,
-            top_p=top_p,
-        )
-        assistant_message = response.choices[0].message['content']
-    except Exception as e:
-        print(f"An error occurred while getting model response: {str(e)}")
-        assistant_message = "An error occurred. Please try again later."
-
-    return assistant_message
-
-demo = gr.ChatInterface(
-    respond,
-    additional_inputs=[
-        gr.Textbox(value="You are an experienced maritime legal assistant.", 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)",
-        ),
-    ],
-    title="Maritime Legal Assistant",
-    description="This chatbot provides legal assistance related to maritime laws using a fine-tuned model hosted on Hugging Face and integrated with Groq API.",
-)
-
-if __name__ == "__main__":
-    demo.launch()
+    print(f"An error occurred: {e}")