Update app.py

app.py

@@ -1,44 +1,44 @@
 import gradio as gr
 from transformers import AutoTokenizer, AutoModelForCausalLM
 
-from transformers import AutoModelForCausalLM, AutoTokenizer
-
-# Load model and tokenizer directly from Hugging Face Hub
-model = AutoModelForCausalLM.from_pretrained("Manasa1/Llama-2-7b-chat-finetune")
-tokenizer = AutoTokenizer.from_pretrained("Manasa1/Llama-2-7b-chat-finetune")
-
-
-def generate_tweet():
-    prompt = "Write a concise, creative tweet reflecting the style and personality in the fine-tuned dataset."
-    # Tokenize the input prompt
-    inputs = tokenizer(prompt, return_tensors="pt", truncation=True, max_length=100, padding=True)
-    
-    # Explicitly set the pad_token_id
-    model.config.pad_token_id = model.config.eos_token_id
-
-    # Generate the tweet with the attention mask
-    outputs = model.generate(
-        inputs["input_ids"],
-        attention_mask=inputs["attention_mask"],  # Pass attention_mask explicitly
-        max_length=140,
-        num_return_sequences=1,
-        top_p=0.8,
-        temperature=0.6,
-        repetition_penalty=1.2,  # Penalize repetition
-    )
-    # Decode and return the generated tweet
-    generated_tweet = tokenizer.decode(outputs[0], skip_special_tokens=True)
-    return generated_tweet.strip()
-
-
-# Gradio Interface
-with gr.Blocks() as app:
-    gr.Markdown("# AI Tweet Generator")
-    gr.Markdown("Click the button below to generate a tweet reflecting the fine-tuned personality.")
-    generate_button = gr.Button("Generate")
-    output_box = gr.Textbox(label="Generated Tweet")
-    
-    generate_button.click(generate_tweet, inputs=None, outputs=output_box)
-
-# Launch the app locally
-app.launch()
+# Load pre-trained model (or fine-tuned model)
+model_name = "Manasa1/GPT_Finetuned_tweets"  # Replace with the fine-tuned model name
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+model = AutoModelForCausalLM.from_pretrained(model_name)
+
+# Function to generate tweets
+def generate_tweet(input_text):
+    prompt = ("You are a tech-savvy, forward-thinking individual with a deep understanding of technology, innovation, and cultural trends. "
+              "Craft a tweet that reflects insightful commentary, wit, or actionable advice based on the following idea: \"{}\". "
+              "Ensure the response is concise, engaging, and suitable for a diverse audience on social media. "
+              "Incorporate elements of thought leadership, futuristic perspectives, and practical wisdom where appropriate.").format(input_text)
+
+    inputs = tokenizer(prompt, return_tensors="pt", max_length=512, truncation=True)
+    outputs = model.generate(inputs['input_ids'], max_length=280, num_return_sequences=1, top_p=0.95, top_k=50)
+    generated_text = tokenizer.decode(outputs[0], skip_special_tokens=True)
+
+    # Extract the tweet text (exclude prompt if included)
+    return generated_text.replace(prompt, "").strip()
+
+# Gradio interface
+def main():
+    with gr.Blocks() as interface:
+        gr.Markdown("""
+        # Tweet Generator
+        Enter a topic or idea, and the AI will craft a tweet inspired by innovative, philosophical, and tech-savvy thought leadership.
+        """)
+
+        with gr.Row():
+            input_text = gr.Textbox(label="Enter your idea or topic:")
+            output_tweet = gr.Textbox(label="Generated Tweet:", interactive=False)
+
+        generate_button = gr.Button("Generate Tweet")
+
+        generate_button.click(generate_tweet, inputs=[input_text], outputs=[output_tweet])
+
+    return interface
+
+# Run Gradio app
+if __name__ == "__main__":
+    app = main()
+    app.launch()