Create app.py

app.py

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+import gradio as gr
+import torch
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+# Load model and tokenizer
+def load_model(model_id):
+    tokenizer = AutoTokenizer.from_pretrained(model_id, trust_remote_code=True)
+    model = AutoModelForCausalLM.from_pretrained(
+        model_id,
+        torch_dtype=torch.float16,
+        device_map="auto",
+        trust_remote_code=True
+    )
+    return model, tokenizer
+
+def generate_response(instruction, model, tokenizer, max_length=200, temperature=0.7, top_p=0.9):
+    # Format the input text
+    input_text = f"### Instruction:\n{instruction}\n\n### Response:\n"
+    
+    # Tokenize input
+    inputs = tokenizer(input_text, return_tensors="pt").to(model.device)
+    
+    # Generate response
+    outputs = model.generate(
+        **inputs,
+        max_length=max_length,
+        temperature=temperature,
+        top_p=top_p,
+        num_return_sequences=1,
+        pad_token_id=tokenizer.eos_token_id
+    )
+    
+    # Decode and return the response
+    response = tokenizer.decode(outputs[0], skip_special_tokens=True)
+    
+    # Extract only the response part
+    response_parts = response.split("### Response:")
+    if len(response_parts) > 1:
+        return response_parts[1].strip()
+    return response.strip()
+
+def create_demo():
+    # Use your uploaded model
+    model_id = "jatingocodeo/phi2-finetuned-openassistant"
+    
+    # Load model and tokenizer
+    model, tokenizer = load_model(model_id)
+    
+    # Define the interface
+    def process_input(instruction, max_length, temperature, top_p):
+        return generate_response(
+            instruction,
+            model,
+            tokenizer,
+            max_length=max_length,
+            temperature=temperature,
+            top_p=top_p
+        )
+    
+    # Create the interface
+    demo = gr.Interface(
+        fn=process_input,
+        inputs=[
+            gr.Textbox(
+                label="Instruction",
+                placeholder="Enter your instruction here...",
+                lines=4
+            ),
+            gr.Slider(
+                minimum=50,
+                maximum=500,
+                value=200,
+                step=10,
+                label="Maximum Length"
+            ),
+            gr.Slider(
+                minimum=0.1,
+                maximum=1.0,
+                value=0.7,
+                step=0.1,
+                label="Temperature"
+            ),
+            gr.Slider(
+                minimum=0.1,
+                maximum=1.0,
+                value=0.9,
+                step=0.1,
+                label="Top P"
+            )
+        ],
+        outputs=gr.Textbox(label="Response", lines=8),
+        title="Phi-2 Fine-tuned Assistant",
+        description="""This is a fine-tuned version of the Microsoft Phi-2 model, trained on the OpenAssistant dataset.
+        You can adjust the generation parameters:
+        - **Maximum Length**: Controls the maximum length of the generated response
+        - **Temperature**: Higher values make the output more random, lower values make it more focused
+        - **Top P**: Controls the cumulative probability threshold for token sampling
+        """,
+        examples=[
+            ["What is machine learning?"],
+            ["Write a short poem about artificial intelligence"],
+            ["Explain quantum computing to a 10-year-old"],
+            ["What are the best practices for writing clean code?"]
+        ]
+    )
+    return demo
+
+if __name__ == "__main__":
+    demo = create_demo()
+    demo.launch()