Update app.py

app.py

@@ -1,90 +1,78 @@
 import gradio as gr
-from transformers import GPT2LMHeadModel, GPT2Tokenizer, pipeline
+from transformers import GPT2Tokenizer, GPT2LMHeadModel
+import re
 
 # 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)
-
-# Define the generator pipeline
-generator = pipeline('text-generation', model=fine_tuned_model, tokenizer=fine_tuned_tokenizer)
-
-# 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"],
+try:
+    model = GPT2LMHeadModel.from_pretrained("Manasa1/finetuned_GPTb")  # Path to your fine-tuned GPT-2 model
+    tokenizer = GPT2Tokenizer.from_pretrained("Manasa1/finetuned_GPTb")  # Path to tokenizer
+    tokenizer.pad_token = tokenizer.eos_token  # Ensure pad_token is set correctly
+except Exception as e:
+    print(f"Error loading model or tokenizer: {e}")
+    exit()
+
+# Function to generate an answer to a question
+def generate_answer(question):
+    if not question.strip():
+        return "Error: Question cannot be empty."
+    try:
+        prompt = f"Q: {question} A:"
+        inputs = tokenizer(prompt, return_tensors="pt", padding=True, truncation=True, max_length=1024)
+        prompt_length = len(inputs["input_ids"][0])
+        max_new_tokens = 1024 - prompt_length
+        output = model.generate(
+            inputs["input_ids"],
+            max_new_tokens=max_new_tokens,
+            num_return_sequences=1,
+            no_repeat_ngram_size=2,
+            top_p=0.9,
+            top_k=50,
+            temperature=0.7,
+            do_sample=True,
+            pad_token_id=tokenizer.eos_token_id
+        )
+        answer = tokenizer.decode(output[0], skip_special_tokens=True)
+        return answer[len(prompt):].strip() if answer else "Error: Could not generate a meaningful response."
+    except Exception as e:
+        return f"Error during generation: {e}"
+
+# Function to add relevant hashtags and emojis
+def add_hashtags_and_emojis(tweet):
+    hashtags_and_emojis = {
+        "AI": ["#AI", "🤖"],
+        "machine learning": ["#MachineLearning", "📊"],
+        "data": ["#DataScience", "📈"],
+        "technology": ["#Tech", "💻"],
+        "innovation": ["#Innovation", "✨"],
+        "coding": ["#Coding", "👨‍💻"],
+        "future": ["#Future", "🔮"],
+        "startup": ["#Startup", "🚀"],
+        "sustainability": ["#Sustainability", "🌱"],
     }
-    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}"
-    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):
-    # 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:
-        tweet = tweet[:280]
-        last_period = tweet.rfind(".")
-        if last_period != -1:
-            tweet = tweet[:last_period + 1]
-    elif tweet_length < 200:
-        tweet = tweet.ljust(200)  # Ensure a minimum length of 200 characters
-
-    # Add relevant hashtags and emojis
-    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 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."
-)
+    tweet_lower = tweet.lower()
+    added_items = []
+    for keyword, items in hashtags_and_emojis.items():
+        if keyword in tweet_lower:
+            added_items.extend(items)
+    added_items = list(dict.fromkeys(added_items))
+    return tweet.strip() + " " + " ".join(added_items)
+
+# Function to handle Gradio input and output
+def generate_tweet_with_hashtags(question):
+    generated_tweet = generate_answer(question)
+    final_tweet = add_hashtags_and_emojis(generated_tweet)
+    return final_tweet
+
+# Gradio app
+with gr.Blocks() as app:
+    gr.Markdown("# AI Tweet Generator with Hashtags and Emojis")
+    gr.Markdown("Enter a question or topic, and the app will generate a tweet and enhance it with relevant hashtags and emojis!")
+    question_input = gr.Textbox(label="Enter your question or topic:")
+    output_tweet = gr.Textbox(label="Generated Tweet with Hashtags and Emojis:", interactive=False)
+    generate_button = gr.Button("Generate Tweet")
+    generate_button.click(generate_tweet_with_hashtags, inputs=[question_input], outputs=[output_tweet])
+
+# Run the app
+if __name__ == "__main__":
+    app.launch()
 
-# Launch the app
-iface.launch()