Create app.py

app.py

@@ -0,0 +1,47 @@
+import torch
+from transformers import AutoTokenizer, AutoModelForSequenceClassification
+import yfinance as yf
+import gradio as gr
+# Load the tokenizer and model
+tokenizer = AutoTokenizer.from_pretrained("huggingface_model")
+model = AutoModelForSequenceClassification.from_pretrained("huggingface_model")
+
+def predict_stock_price(day, month, year, input_text):
+    # Get the stock symbol from the input text
+    stock_symbol = extract_stock_symbol(input_text)
+
+    # Format the date for yfinance
+    start_date = f"{year}-{month:02d}-{day:02d}"
+    end_date = f"{year}-{month:02d}-{day:02d}"
+
+    # Get the historical stock data using yfinance
+    stock_data = yf.download(stock_symbol, start=start_date, end=end_date)
+
+    # Perform any necessary data preprocessing
+
+    # Tokenize the input text
+    inputs = tokenizer(input_text, return_tensors="pt")
+
+    # Forward pass through the model
+    outputs = model(**inputs)
+
+    # Get the predicted stock price
+    predicted_price = outputs.logits.item()
+
+    return predicted_price
+
+def extract_stock_symbol(input_text):
+    # Implement the logic to extract the stock symbol from the input text
+    # For example, you can use regular expressions or a custom parsing algorithm
+    pass
+
+# Define the Gradio interface
+inputs = [
+    gr.inputs.Number(label="Day", min_value=1, max_value=31),
+    gr.inputs.Number(label="Month", min_value=1, max_value=12),
+    gr.inputs.Number(label="Year", min_value=2000, max_value=2022),
+    gr.inputs.Textbox(label="Input Text")
+]
+output = gr.outputs.Textbox(label="Predicted Stock Price")
+
+gr.Interface(fn=predict_stock_price, inputs=inputs, outputs=output).launch()