Create app.py

app.py

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+import streamlit as st
+from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
+
+# Step 1: Load the fine-tuned model and tokenizer
+MODEL_NAME = "ridahabbash/AccR4"  # Replace with your model's Hub ID
+tokenizer = AutoTokenizer.from_pretrained(MODEL_NAME)
+model = AutoModelForSeq2SeqLM.from_pretrained(MODEL_NAME)
+
+# Step 2: Define the prediction function
+def generate_report(prompt):
+    # Tokenize input
+    inputs = tokenizer(prompt, return_tensors="pt", max_length=512, truncation=True)
+
+    # Generate output
+    outputs = model.generate(**inputs, max_length=128)
+
+    # Decode and return the result
+    report = tokenizer.decode(outputs[0], skip_special_tokens=True)
+    return report
+
+# Step 3: Create the Streamlit interface
+st.title("Accounting Report Generator")
+st.markdown("Enter a ledger entry below, and the model will generate a report.")
+
+# Input textbox
+prompt = st.text_area("Ledger Entry", placeholder="Enter ledger details here...")
+
+# Generate button
+if st.button("Generate Report"):
+    if prompt.strip() == "":
+        st.error("Please enter a valid ledger entry.")
+    else:
+        # Generate report
+        with st.spinner("Generating report..."):
+            report = generate_report(prompt)
+        # Display the report
+        st.success("Report generated successfully!")
+        st.subheader("Generated Report:")
+        st.write(report)
+
+