Update app.py

app.py

@@ -1,7 +1,71 @@
 import gradio as gr
 
 def greet(name):
-    return "Hello " + name + "!!"
+    f = hello()
+    return f
+
+def hello():
+    from transformers import AutoModelForCausalLM, AutoTokenizer
+
+    # Using pandas to read some structured data
+    import pandas as pd
+    from io import StringIO
+    
+    # single table
+    EXAMPLE_CSV_CONTENT = """
+    "Loss","Date","Score","Opponent","Record","Attendance"
+    "Hampton (14–12)","September 25","8–7","Padres","67–84","31,193"
+    "Speier (5–3)","September 26","3–1","Padres","67–85","30,711"
+    "Elarton (4–9)","September 22","3–1","@ Expos","65–83","9,707"
+    "Lundquist (0–1)","September 24","15–11","Padres","67–83","30,774"
+    "Hampton (13–11)","September 6","9–5","Dodgers","61–78","31,407"
+    """
+    
+    csv_file = StringIO(EXAMPLE_CSV_CONTENT)
+    df = pd.read_csv(csv_file)
+    
+    model_name = "tablegpt/TableGPT2-7B"
+    
+    model = AutoModelForCausalLM.from_pretrained(
+        model_name, torch_dtype="auto", device_map="auto"
+    )
+    tokenizer = AutoTokenizer.from_pretrained(model_name)
+    
+    example_prompt_template = """Given access to several pandas dataframes, write the Python code to answer the user's question.
+    
+    /*
+    "{var_name}.head(5).to_string(index=False)" as follows:
+    {df_info}
+    */
+    
+    Question: {user_question}
+    """
+    question = "哪些比赛的战绩达到了40胜40负？"
+    
+    prompt = example_prompt_template.format(
+        var_name="df",
+        df_info=df.head(5).to_string(index=False),
+        user_question=question,
+    )
+    
+    messages = [
+        {"role": "system", "content": "You are a helpful assistant."},
+        {"role": "user", "content": prompt},
+    ]
+    text = tokenizer.apply_chat_template(
+        messages, tokenize=False, add_generation_prompt=True
+    )
+    model_inputs = tokenizer([text], return_tensors="pt").to(model.device)
+    
+    generated_ids = model.generate(**model_inputs, max_new_tokens=512)
+    generated_ids = [
+        output_ids[len(input_ids) :]
+        for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
+    ]
+    
+    response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]
+    return response
+
 
 demo = gr.Interface(fn=greet, inputs="text", outputs="text")
 demo.launch()