Update app.py

app.py

@@ -1,5 +1,6 @@
 import gradio as gr
 from transformers import pipeline, GPT2LMHeadModel, GPT2Tokenizer
+import re
 
 # Load the fine-tuned GPT-2 model and tokenizer
 model_dir = "Manasa1/finetuned_GPT23"
@@ -9,21 +10,65 @@ fine_tuned_tokenizer = GPT2Tokenizer.from_pretrained(model_dir)
 # Create a text-generation pipeline
 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):
+    # Ensure the tweet is concise, ending on a complete sentence
+    if len(tweet) > max_length:
+        tweet = tweet[:max_length]
+        last_period = tweet.rfind(".")
+        if last_period != -1:
+            tweet = tweet[:last_period + 1]
+    return tweet.strip()
+
+# Function to intelligently add relevant hashtags and emojis
+def add_relevant_tags(tweet, input_question):
+    # Pre-defined mappings of topics to hashtags and emojis
+    topic_to_hashtags = {
+        "startup": ["#Startups", "#Innovation", "#Entrepreneurship"],
+        "AI": ["#AI", "#ArtificialIntelligence", "#Tech"],
+        "technology": ["#Technology", "#Future", "#Tech"],
+        "future": ["#Future", "#Vision", "#Tech"],
+    }
+    topic_to_emojis = {
+        "startup": "🚀",
+        "AI": "🤖",
+        "technology": "💻",
+        "future": "🌟",
+    }
+
+    # Determine topic from input question (using keywords)
+    topic = None
+    for key in topic_to_hashtags.keys():
+        if key.lower() in input_question.lower():
+            topic = key
+            break
+
+    # Add relevant hashtags and emoji if a topic is detected
+    if topic:
+        hashtags = " ".join(topic_to_hashtags[topic][:2])  # Take up to 2 hashtags
+        emoji = topic_to_emojis[topic]
+        tweet = f"{tweet} {emoji} {hashtags}"
+    return tweet.strip()
+
 def generate_tweet(input_question):
-    # Format the prompt
-    prompt = f"Question: {input_question} Answer:"
+    # Format the input without "Question:" and "Answer:"
+    prompt = input_question.strip()
     # Generate the output
-    output = generator(prompt, max_length=250, num_return_sequences=1, temperature=0.9, top_p=0.9)
-    # Extract and return the generated text
-    return output[0]['generated_text']
+    output = generator(prompt, max_length=150, 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
+    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 startup."),
+    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 tweets based on prompts using a fine-tuned GPT-2 model."
+    description="Generate concise, relevant tweets enriched with appropriate emojis and hashtags using a fine-tuned GPT-2 model."
 )
 
 # Launch the interface