Upload app.py

app.py

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+import gradio as gr
+from transformers import pipeline, set_seed
+import torch
+
+# Function to generate responses using the entire conversation history
+def generate_response(messages, model_name, sampling_temperature, max_tokens, top_p):
+    generator = pipeline('text-generation', model=model_name, torch_dtype=torch.float16)
+    set_seed(42)  # You can set a different seed for reproducibility
+    
+    # Combine entire conversation history
+    conversation = ""
+    for message in messages:
+        role = message['role']
+        content = message['content']
+        conversation += f"<|im_start|>{role}\n{content}<|im_end|>\n"
+    
+    # Generate response
+    response = generator(conversation, max_length=2048, temperature=sampling_temperature, max_tokens=max_tokens, top_p=top_p, repetition_penalty=1.1, top_k=12)
+    
+    return [{'content': response[0]['generated_text'], 'role': 'assistant'}]
+
+# Gradio chatbot interface with conversation history
+iface = gr.Interface(
+    fn=generate_response,
+    inputs=[
+        gr.Chat("You", "Chatbot"),
+        gr.Dropdown("Select Model", ["Locutusque/TinyMistral-248M-v2.5-Instruct", "Locutusque/Hercules-1.0-Mistral-7B", "Locutusque/UltraQwen-1_8B"]),
+        gr.Slider("Sampling Temperature", 0.1, 2.0, 1.0, 0.1),
+        gr.Slider("Max Tokens", 5, 200, 50, 5),
+        gr.Slider("Top P", 0.1, 0.5, 0.75, 0.1)
+    ],
+    outputs=gr.Chat(role="Chatbot"),
+    live=True,
+    capture_session=True
+)
+
+# Launch Gradio chatbot interface
+iface.launch()
+