Create app.py

app.py

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+from transformers import AutoModelForCausalLM, AutoTokenizer
+import gradio as gr
+
+# Load pre-trained model and tokenizer
+model_name = "mistralai/Mistral-7B-Instruct-v0.2"
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+model = AutoModelForCausalLM.from_pretrained(model_name)
+
+def generate_response(messages):
+    """
+    Generate response based on the given user messages.
+    Parameters:
+    - messages (list): A list of dictionaries containing user messages with roles.
+    Returns:
+    - response (str): The generated response.
+    """
+    # Apply chat template and encode messages
+    encodeds = tokenizer.apply_chat_template(messages, return_tensors="pt")
+    # Move inputs to device
+    model_inputs = encodeds.to("cuda")  # Assuming CUDA device is available
+    # Generate response
+    generated_ids = model.generate(model_inputs, max_new_tokens=1000, do_sample=True)
+    # Decode the generated response
+    response = tokenizer.batch_decode(generated_ids)[0]
+    return response
+
+# Define Gradio interface components
+input_chat = gr.Textbox(lines=5, label="Input Chat", placeholder="Enter chat messages...")
+output_response = gr.Textbox(label="Generated Response", placeholder="Generated response will appear here...")
+
+# Create Gradio interface
+gr.Interface(generate_response, input_chat, output_response,
+             title="Chat Response Generation",
+             description="Generate responses based on user messages using Mistral AI model.",
+             theme="default",
+             allow_flagging="never").launch()