Create app.py

app.py

@@ -0,0 +1,53 @@
+import gradio as gr
+from transformers import AutoModelForCausalLM, AutoTokenizer
+import torch
+
+# Load the saved model and tokenizer
+model_path = "GouthamVarma/mentalhealth_coversational_chatbot"  # You'll need to upload your model to HF Hub first
+tokenizer = AutoTokenizer.from_pretrained(model_path)
+model = AutoModelForCausalLM.from_pretrained(
+    model_path,
+    device_map="auto",
+    torch_dtype=torch.float16,
+    trust_remote_code=True
+)
+
+def chat_response(message, history):
+    formatted_prompt = f"User: {message}\nAssistant: "
+    
+    inputs = tokenizer(formatted_prompt, return_tensors="pt", truncation=True, max_length=512)
+    inputs = {k: v.to(model.device) for k, v in inputs.items()}
+    
+    outputs = model.generate(
+        **inputs,
+        max_length=512,
+        num_return_sequences=1,
+        temperature=0.7,
+        do_sample=True,
+        top_p=0.85,
+        top_k=40,
+        no_repeat_ngram_size=3,
+        repetition_penalty=1.3,
+        pad_token_id=tokenizer.eos_token_id
+    )
+    
+    response = tokenizer.decode(outputs[0], skip_special_tokens=True)
+    response = response.split("Assistant: ")[-1].strip()
+    
+    return response
+
+# Create Gradio Interface
+demo = gr.ChatInterface(
+    fn=chat_response,
+    title="Mental Health Support Assistant",
+    description="A supportive AI assistant trained to provide empathetic responses to mental health concerns. Please note: This is not a replacement for professional mental health support.",
+    theme="soft",
+    examples=[
+        "I've been feeling really anxious lately about work.",
+        "I can't sleep at night because of stress.",
+        "I feel lonely and isolated."
+    ]
+)
+
+if __name__ == "__main__":
+    demo.launch()