Create app.py

app.py

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+import streamlit as st
+from transformers import pipeline, AutoTokenizer
+import base64
+
+# Load the GPT-2 text generation model from Hugging Face
+text_generator = pipeline("text-generation", model="Shruti9756/G24_Contract_Summarization_step3")
+
+# Increase the maximum token limit
+tokenizer = AutoTokenizer.from_pretrained("Shruti9756/G24_Contract_Summarization_step3")
+text_generator.model.config.max_position_embeddings = tokenizer.model_max_length
+
+# Function to generate text using the GPT-2 model
+def generate_text(prompt):
+    generated_text = text_generator(prompt, max_length=300, num_return_sequences=1, temperature=0.7)[0]['generated_text']
+    return generated_text
+
+# # Function to handle feedback and store it in a CSV file
+# def handle_feedback(feedback_data, feedback_file):
+#     # Your existing feedback handling logic remains the same
+
+# # Function to create a download link for a binary file
+# def get_binary_file_downloader_html(file_path, file_label):
+#     # Your existing file downloader logic remains the same
+
+# Main Streamlit app
+def main():
+    st.title("Text Generation with Feedback")
+
+    # Input area for prompt
+    prompt = st.text_area("Enter a prompt:", height=100)
+
+    # Button to generate text
+    if st.button("Generate Text"):
+        generated_text = generate_text(prompt)
+        st.subheader("Generated Text:")
+        st.write(generated_text)
+
+        # Feedback section
+        st.subheader("Feedback:")
+        thumbs_up = st.button("👍")
+        thumbs_down = st.button("👎")
+
+        chosen = "👍" if thumbs_up else None
+        rejected = "👎" if thumbs_down else None
+
+        # feedback_data.append((prompt, generated_text, chosen, rejected))
+
+        # Handle feedback data
+        # if feedback_data:
+        #     feedback_file = 'feedback.csv'
+        #     st.session_state.feedback_csv_exists = True
+        #     handle_feedback(feedback_data, feedback_file)
+
+# Initialize feedback data
+# feedback_data = []
+
+# Run the app
+if __name__ == "__main__":
+    main()