Create app.py

app.py

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+from transformers import GPT2Tokenizer, GPT2LMHeadModel
+import gradio as gr
+
+# Load the saved tokenizer and model
+tokenizer = GPT2Tokenizer.from_pretrained("Rehman1603/new_crm_fine_tuned_gpt2_model")
+model = GPT2LMHeadModel.from_pretrained("Rehman1603/new_crm_fine_tuned_gpt2_model")
+
+# Function to generate responses
+def generate_response(question, max_length=150):
+    input_text = f"<startofstring> {question} <bot>:"
+    input_ids = tokenizer.encode(input_text, return_tensors="pt")
+    output = model.generate(
+        input_ids,
+        max_length=max_length,
+        num_return_sequences=1,
+        no_repeat_ngram_size=2,  # Prevent repeating n-grams
+        top_k=50,  # Limit sampling to the top-k tokens
+        top_p=0.95,  # Use nucleus sampling
+        temperature=0.7,  # Control randomness
+        do_sample=True,  # Enable sampling
+    )
+    response = tokenizer.decode(output[0], skip_special_tokens=True)
+    # Extract only the bot's response
+    response = response.split("<bot>:")[-1].strip()
+    return response
+
+# Function to handle chat interaction
+def chat(message, history):
+    # Generate a response using the model
+    response = generate_response(message)
+    return response
+
+# Example questions
+examples = [
+    "Hello! Can I get more info?",
+    "Restaurant",
+    "online or offline?",
+    "isky charges kia ha",
+]
+
+# Gradio ChatInterface
+demo = gr.ChatInterface(
+    fn=chat,  # Function to handle chat
+    examples=examples,  # Example questions
+    title="Chat with the Fine-Tuned GPT-2 Model",  # Title of the interface
+    description="Ask me anything about the software!",  # Description
+)
+
+# Launch the Gradio app
+demo.launch()