Update app.py

app.py

@@ -1,23 +1,26 @@
-# Define the messages properly before using them
-messages = [
-    {"role": "system", "content": "You are a helpful assistant."},
-    {"role": "user", "content": "Hello!"},
-]
+import gradio as gr
+from transformers import AutoModelForCausalLM, AutoTokenizer
+import torch
 
-# Stream the response from the model
-def stream_response():
-    response = ""
-    # Make sure to define 'messages' before using it
-    for message in client.chat_completion(
-        messages,
-        max_tokens=max_tokens,
-        stream=True,
-        temperature=temperature,
-        top_p=top_p,
-    ):
-        token = message.choices[0].delta.content
-        response += token
-        yield response
-    
-for resp in stream_response():
-    print(resp)
+# Load the model and tokenizer from Hugging Face
+model_name = "fahadMizan/chatbot"
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+model = AutoModelForCausalLM.from_pretrained(model_name)
+
+# Define the chatbot function
+def chatbot(input_text):
+    inputs = tokenizer(input_text, return_tensors="pt")
+    outputs = model.generate(inputs["input_ids"], max_length=100)
+    response = tokenizer.decode(outputs[0], skip_special_tokens=True)
+    return response
+
+# Create the Gradio interface
+iface = gr.Interface(fn=chatbot, 
+                     inputs="text", 
+                     outputs="text", 
+                     title="Fahad Chatbot",
+                     description="A chatbot using the 'fahadMizan/chatbot' model from Hugging Face.")
+
+# Launch the app
+if __name__ == "__main__":
+    iface.launch()