Update app.py

app.py

@@ -1,13 +1,12 @@
 import gradio as gr
-from transformers import pipeline, GPT2LMHeadModel, GPT2Tokenizer
-import re
+from transformers import GPT2LMHeadModel, GPT2Tokenizer, pipeline
 
-# Load the fine-tuned GPT-2 model and tokenizer
-model_dir = "Manasa1/finetuned_GPT23"
+# Load the fine-tuned model and tokenizer
+model_dir = "Manasa1/finetuned_GPT2w"
 fine_tuned_model = GPT2LMHeadModel.from_pretrained(model_dir)
 fine_tuned_tokenizer = GPT2Tokenizer.from_pretrained(model_dir)
 
-# Create a text-generation pipeline
+# Define the generator pipeline
 generator = pipeline('text-generation', model=fine_tuned_model, tokenizer=fine_tuned_tokenizer)
 
 # Function to intelligently add relevant hashtags and emojis
@@ -38,18 +37,26 @@ def add_relevant_tags(tweet, input_question):
         hashtags = " ".join(topic_to_hashtags[topic][:2])  # Take up to 2 hashtags
         emoji = topic_to_emojis[topic]
         tweet = f"{tweet} {emoji} {hashtags}"
+    else:
+        # If no topic is detected, don't add emojis/hashtags
+        tweet = f"{tweet} #NoTopic"
+
     return tweet.strip()
 
+# Function to generate tweet
 def generate_tweet(input_question):
-    # Format the input without "Question:" and "Answer:"
-    prompt = input_question.strip()
-    
-    # Generate the output with a max length of around 200 to 280 characters
-    output = generator(prompt, max_length=300, num_return_sequences=1, temperature=0.7, top_p=0.9)
-    
+    # Formulate the prompt with clear guidance for tweet generation
+    input_text = f"Write a very short, engaging tweet with emojis and relevant hashtags about {input_question}. Keep it between 200 and 280 characters. Provide only the tweet."
+
+    # Generate the output using the pipeline
+    output = generator(input_text, max_length=280, num_return_sequences=1, temperature=0.7, top_p=0.9)
+
     # Extract the generated text
     tweet = output[0]['generated_text']
-    
+
+    # Extract the tweet part by splitting based on the prompt
+    tweet = tweet.split(f"Write a very short, engaging tweet with emojis and relevant hashtags about {input_question}")[-1].strip()
+
     # Ensure the tweet is between 200 and 280 characters
     tweet_length = len(tweet)
     if tweet_length > 280:
@@ -58,21 +65,26 @@ def generate_tweet(input_question):
         if last_period != -1:
             tweet = tweet[:last_period + 1]
     elif tweet_length < 200:
-        tweet = tweet.ljust(200)
+        tweet = tweet.ljust(200)  # Ensure a minimum length of 200 characters
 
     # Add relevant hashtags and emojis
-    tweet = add_relevant_tags(tweet, input_question) 
+    tweet = add_relevant_tags(tweet, input_question)
+
+    return tweet
 
+# Gradio interface
+def gradio_interface(input_question):
+    tweet = generate_tweet(input_question)
     return tweet
 
-# Create the Gradio interface
-interface = gr.Interface(
-    fn=generate_tweet,
-    inputs=gr.Textbox(label="Enter a prompt/question", placeholder="Write a tweet about AI."),
-    outputs=gr.Textbox(label="Generated Tweet"),
-    title="Tweet Generator",
-    description="Generate concise, relevant tweets enriched with appropriate emojis and hashtags using a fine-tuned GPT-2 model."
+# Create the Gradio app
+iface = gr.Interface(
+    fn=gradio_interface, 
+    inputs="text", 
+    outputs="text", 
+    title="AI Tweet Generator",
+    description="Enter a topic, and the model will generate a tweet with relevant hashtags and emojis."
 )
 
-# Launch the interface
-interface.launch()
+# Launch the app
+iface.launch()