Update app.py

app.py

@@ -1,27 +1,86 @@
-# test_groq_api.py
 
-import os
+# app.py
+
+import gradio as gr
+from transformers import AutoTokenizer
 from groq import Groq
+import os
+from huggingface_hub import login
 
 # Initialize Groq API client
-client = Groq(api_key=os.environ["GROQ_API_KEY"])
+try:
+    client = Groq(api_key=os.environ["GROQ_API_KEY"])
+except KeyError:
+    raise ValueError("GROQ_API_KEY environment variable not set.")
 
-# Model identifier for Groq API
-model_name = "asthaa30/l3.1"
+# 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.")
+
+# Authentication token for Hugging Face
+login(token=hf_token)
 
-messages = [
-    {"role": "system", "content": "You are a friendly Chatbot."},
-    {"role": "user", "content": "Hello!"},
-]
+# Model identifier for Groq API (you can replace it with your HF model if needed)
+model_name = "asthaa30/l3.1"
 
+# Load tokenizer (model will be accessed via Groq API)
 try:
-    response = client.chat.completions.create(
-        model=model_name,
-        messages=messages,
-        max_tokens=512,
-        temperature=0.7,
-        top_p=0.95,
-    )
-    print(response)
+    tokenizer = AutoTokenizer.from_pretrained(model_name, token=hf_token)
 except Exception as e:
-    print(f"An error occurred: {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()