demo interface

app.py

@@ -1,30 +1,80 @@
+import re
 import gradio as gr
+from transformers import AutoTokenizer, AutoModelForCausalLM
+from peft import PeftModel
+from datetime import date
+
+
+base_model = AutoModelForCausalLM.from_pretrained(
+    'meta-llama/Llama-2-7b-chat-hf',
+    trust_remote_code=True, 
+    device_map="auto",
+)
+model = PeftModel.from_pretrained(
+    base_model,
+    'FinGPT/fingpt-forecaster_dow30_llama2-7b_lora'
+)
+model = model.eval()
+
+tokenizer = AutoTokenizer.from_pretrained('meta-llama/Llama-2-7b-chat-hf')
+
+
+def construct_prompt(ticker, date, n_weeks):
+    
+    return ", ".join([ticker, date, str(n_weeks)])
 
-# Assuming `my_gpt_model` is your custom model's function that takes a date range and a ticker and returns a string.
-def predict_with_gpt(start_date, end_date, ticker):
-    # Your model would use the start_date, end_date, and ticker to generate a prediction.
-    # For this example, we'll just return a dummy string.
-    prediction = ", ".join([start_date, end_date, ticker])
-    return prediction
-
-# Create the Gradio app
-def create_gradio_app():
-    with gr.Blocks() as app:
-        gr.Markdown("Enter a range of dates and a stock ticker to get predictions from the GPT model.")
-        with gr.Row():
-            start_date = gr.Date(label="Start Date")
-            end_date = gr.Date(label="End Date")
-        ticker = gr.Textbox(label="Ticker")
-        output = gr.Textbox(label="GPT Model Output")
-
-        # When the button is clicked, the `predict_with_gpt` function is called
-        gr.Button("Predict").click(
-            predict_with_gpt, 
-            inputs=[start_date, end_date, ticker], 
-            outputs=output
-        )
 
-    return app
+def get_curday():
+    
+    return date.today().strftime("%Y-%m-%d")
+
+
+def predict(ticker, date, n_weeks):
+    
+    prompt = construct_prompt(ticker, date, n_weeks)
+      
+#     inputs = tokenizer(
+#         prompt, return_tensors='pt',
+#         padding=False, max_length=4096
+#     )
+#     inputs = {key: value.to(model.device) for key, value in inputs.items()}
+        
+#     res = model.generate(
+#         **inputs, max_length=4096, do_sample=True,
+#         eos_token_id=tokenizer.eos_token_id,
+#         use_cache=True
+#     )
+#     output = tokenizer.decode(res[0], skip_special_tokens=True)
+#     answer = re.sub(r'.*\[/INST\]\s*', '', output, flags=re.DOTALL)
+
+    answer = prompt
+    
+    return answer
+
+
+demo = gr.Interface(
+    predict,
+    inputs=[
+        gr.Textbox(
+            label="Ticker",
+            value="AAPL",
+            info="Companys from Dow-30 are recommended"
+        )
+        gr.Textbox(
+            label="Date",
+            value=get_curday,
+            info="Date from which the prediction is made, use format 'yyyy-mm-dd'"
+        ),
+        gr.Slider(
+            minimum=1,
+            maximum=4,
+            value=3,
+            step=1,
+            label="n_weeks",
+            info="Information of the past n weeks will be utilized, choose between 1 and 4"
+        ),
+    ],
+    outputs="Response"
+)
 
-app = create_gradio_app()
-app.launch()
+demo.launch()