Update app.py

app.py

@@ -1,32 +1,33 @@
 import streamlit as st
-from transformers import AutoModelForSeq2SeqLM, AutoTokenizer
-
-# Load pre-trained model and tokenizer
-model_name = "Laksitha/autotrain-tosdr_tldr_legal_summarisation_v1-1434353657"
-tokenizer = AutoTokenizer.from_pretrained(model_name)
-model = AutoModelForSeq2SeqLM.from_pretrained(model_name)
-
-# Streamlit app
-st.title("Legal Summary Generator")
-
-# User input
-user_input = st.text_area("Enter legal text:")
-
-# Generate summary
-if st.button("Generate Summary"):
-    if user_input:
-        # Tokenize input and generate summary
-        input_ids = tokenizer.encode(user_input, return_tensors="pt", max_length=1024, truncation=True)
-        output = model.generate(input_ids, max_length=150, num_beams=2, length_penalty=0.8, early_stopping=True)
-
-        # Decode and display generated summary
-        generated_summary = tokenizer.decode(output[0], skip_special_tokens=True)
-        st.write("Generated Summary:")
-        st.write(generated_summary)
-    else:
-        st.warning("Please enter legal text for summarization.")
-
-# Additional features or explanations
-st.write("\n\n")
-st.subheader("About This Demo")
-st.write("This is a simple Streamlit app for generating legal summaries.")
+import pandas as pd
+import os
+
+# Function to store feedback in a file
+def store_feedback(contract_text, feedback, filename='feedback_data.csv'):
+    feedback_data = pd.read_csv(filename) if os.path.exists(filename) else pd.DataFrame(columns=['Contract', 'Feedback'])
+
+    feedback_data = feedback_data.append({'Contract': contract_text, 'Feedback': feedback}, ignore_index=True)
+    feedback_data.to_csv(filename, index=False)
+
+# Main Streamlit app
+def main():
+    st.title("Contract Summarizer with Feedback")
+
+    # Get contract input from user
+    contract_text = st.text_area("Paste your contract here:")
+
+    # Display the input contract
+    st.subheader("Input Contract:")
+    st.write(contract_text)
+
+    # Get feedback from the user using emojis
+    feedback = st.radio("Feedback:", ['👍', '👎'])
+
+    if st.button("Submit Feedback"):
+        # Store the feedback in a file
+        store_feedback(contract_text, feedback)
+        st.success("Feedback submitted successfully!")
+
+# Run the app
+if __name__ == "__main__":
+    main()