Update app.py

app.py

@@ -58,6 +58,48 @@ demo = gr.ChatInterface(
         ),
     ],
 )
+import gradio as gr
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+# Load your fine-tuned GPT-2 model from Hugging Face
+MODEL_NAME = "hackergeek98/therapist01"  # Replace w 
+tokenizer = AutoTokenizer.from_pretrained(MODEL_NAME)
+model = AutoModelForCausalLM.from_pretrained(MODEL_NAME)
+
+# Initialize conversation history
+conversation_history = ""
+
+# Function to generate responses
+def generate_response(user_input):
+    global conversation_history
+
+    # Update conversation history with user input
+    conversation_history += f"User: {user_input}\n"
+    
+    # Tokenize the conversation history
+    inputs = tokenizer(conversation_history, return_tensors="pt", truncation=True, max_length=1024)
+
+    # Generate a response from the model
+    outputs = model.generate(inputs['input_ids'], max_length=1024, num_return_sequences=1, no_repeat_ngram_size=2)
+
+    # Decode the model's output
+    response = tokenizer.decode(outputs[0], skip_special_tokens=True)
+
+    # Update conversation history with the model's response
+    conversation_history += f"Therapist: {response}\n"
+
+    # Return the therapist's response
+    return response
+
+# Create Gradio interface
+interface = gr.Interface(fn=generate_response,
+                         inputs=gr.Textbox(label="Enter your message", lines=2),
+                         outputs=gr.Textbox(label="Therapist Response", lines=2),
+                         title="Virtual Therapist",
+                         description="A fine-tuned GPT-2 model acting as a virtual therapist. Chat with the model and receive responses as if you are talking to a therapist.")
+
+# Launch the app
+interface.launch()
 
 
 if __name__ == "__main__":