Create app.py

app.py

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+import streamlit as st
+import torch
+from transformers import Qwen2_5OmniForConditionalGeneration, Qwen2_5OmniProcessor
+
+# Load the model and processor
+model = Qwen2_5OmniForConditionalGeneration.from_pretrained("Qwen/Qwen2.5-Omni-7B", torch_dtype="auto", device_map="auto")
+processor = Qwen2_5OmniProcessor.from_pretrained("Qwen/Qwen2.5-Omni-7B")
+
+# Streamlit app title
+st.title("Cryptocurrency Price Prediction")
+
+# User input for cryptocurrency and time frame
+crypto = st.text_input("Enter Cryptocurrency (e.g., Bitcoin, Ethereum):")
+time_frame = st.selectbox("Select Time Frame:", ["1 Hour", "1 Day", "1 Week", "1 Month"])
+
+# Button to predict price
+if st.button("Predict Price"):
+    if crypto:
+        # Prepare input for the model
+        input_text = f"Predict the price of {crypto} for the next {time_frame}."
+        inputs = processor(input_text, return_tensors="pt", padding=True).to(model.device)
+
+        # Generate prediction
+        with torch.no_grad():
+            output = model.generate(**inputs)
+        
+        # Decode the output
+        prediction = processor.batch_decode(output, skip_special_tokens=True)[0]
+        
+        # Display the prediction
+        st.success(f"The predicted price of {crypto} for the next {time_frame} is: {prediction}")
+    else:
+        st.error("Please enter a cryptocurrency name.")