Update app.py

app.py

@@ -11,7 +11,7 @@ fine_tuned_tokenizer = GPT2Tokenizer.from_pretrained(model_dir)
 generator = pipeline('text-generation', model=fine_tuned_model, tokenizer=fine_tuned_tokenizer)
 
 # Function to dynamically truncate output while keeping it meaningful
-def truncate_tweet(tweet, max_length=240):
+def truncate_tweet(tweet, max_length=280):
     # Ensure the tweet is concise, ending on a complete sentence
     if len(tweet) > max_length:
         tweet = tweet[:max_length]
@@ -53,13 +53,23 @@ def add_relevant_tags(tweet, input_question):
 def generate_tweet(input_question):
     # Format the input without "Question:" and "Answer:"
     prompt = input_question.strip()
-    # Generate the output
-    output = generator(prompt, max_length=150, num_return_sequences=1, temperature=0.7, top_p=0.9)
+    
+    # Generate the output with a higher max_length for longer responses
+    output = generator(prompt, max_length=300, num_return_sequences=1, temperature=0.7, top_p=0.9)
+    
     # Extract the generated text and clean it
     tweet = output[0]['generated_text']
-    tweet = re.sub(r"(Question:|Answer:)", "", tweet).strip()  # Remove "Question:" and "Answer:"
-    tweet = truncate_tweet(tweet)  # Truncate to ensure it's concise
-    tweet = add_relevant_tags(tweet, input_question)  # Add relevant hashtags and emojis
+    
+    # Remove "Question:" and "Answer:" from the generated text
+    tweet = re.sub(r"(Question:|Answer:|A:|)", "", tweet).strip()
+
+    # Remove any part of the tweet that starts with the input question
+    tweet = tweet.replace(input_question, "").strip()
+
+    # Truncate and add relevant hashtags and emojis
+    tweet = truncate_tweet(tweet)
+    tweet = add_relevant_tags(tweet, input_question) 
+
     return tweet
 
 # Create the Gradio interface
@@ -72,4 +82,4 @@ interface = gr.Interface(
 )
 
 # Launch the interface
-interface.launch()
+interface.launch()