Update app.py

app.py

@@ -1,33 +1,86 @@
 import gradio as gr
-from transformers import AutoModelForCausalLM, AutoTokenizer
+from huggingface_hub import InferenceClient
+import os
+import ssl
 
-# Load Mistral 7B
-model_name = "mistralai/Mistral-7B-Instruct-v0.1"
-tokenizer = AutoTokenizer.from_pretrained(model_name)
-model = AutoModelForCausalLM.from_pretrained(model_name)
+# Ensure SSL module is available
+ssl._create_default_https_context = ssl._create_unverified_context
 
+# Load Hugging Face API Token from Environment Variables
+HF_TOKEN = os.getenv("HF_TOKEN")
+
+# Initialize Mistral-7B Model
+client = InferenceClient("mistralai/Mistral-7B-Instruct-v0.1", token=HF_TOKEN)
+
+# Function to handle chat
 def respond(message, history):
-    # Format conversation history
     messages = []
+    
     for user_msg, bot_msg in history:
-        messages.append({"role": "user", "content": user_msg})
+        if user_msg:
+            messages.append({"role": "user", "content": user_msg})
         if bot_msg:
             messages.append({"role": "assistant", "content": bot_msg})
+
     messages.append({"role": "user", "content": message})
+
+    response = ""
+    chat_response = client.chat_completion(
+        messages,
+        max_tokens=512,  # Default max tokens
+        stream=False,  # Change to True if streaming works
+        temperature=0.7,  # Default temperature
+        top_p=0.95,  # Default top-p value
+    )
     
-    # Tokenize and generate response
-    inputs = tokenizer.apply_chat_template(messages, return_tensors="pt")
-    outputs = model.generate(inputs, max_new_tokens=200)
-    response = tokenizer.decode(outputs[0], skip_special_tokens=True)
+    if hasattr(chat_response, "choices") and chat_response.choices:
+        response = chat_response.choices[0].message.content
     
     return response
 
-# Create Gradio interface
-demo = gr.ChatInterface(
-    fn=respond,
-    title="Mistral 7B Chatbot",
-    description="Ask me anything!"
-)
+# Custom Styling for Dark Mode
+custom_css = """
+body {
+    background-color: #121212;
+    color: white;
+    font-family: 'Arial', sans-serif;
+}
+.gradio-container {
+    max-width: 700px;
+    margin: auto;
+    padding: 20px;
+    background: #1E1E1E;
+    border-radius: 10px;
+    box-shadow: 0px 4px 10px rgba(0, 0, 0, 0.2);
+}
+h1 {
+    font-size: 24px;
+    font-weight: bold;
+    text-align: left;
+    color: #00ccff;
+}
+h2 {
+    text-align: center;
+    font-size: 30px;
+    font-weight: bold;
+    color: white;
+}
+.watermark {
+    text-align: center;
+    font-size: 14px;
+    color: gray;
+    margin-top: 20px;
+}
+"""
+
+# Gradio Chat Interface
+with gr.Blocks(css=custom_css) as demo:
+    gr.Markdown("<h1>Mistral AI Chatbot</h1>")  # Top left title
+    gr.Markdown("<h2>How can I help you?</h2>")  # Center title
+
+    chatbot = gr.ChatInterface(respond)
+    
+    gr.Markdown('<div class="watermark">Created by Rajma</div>')
 
-if __name__ == "__main__":
+if _name_ == "_main_":
     demo.launch()