Update app.py

app.py

@@ -1,63 +1,29 @@
 import gradio as gr
-from huggingface_hub import InferenceClient
-
-"""
-For more information on `huggingface_hub` Inference API support, please check the docs: https://huggingface.co/docs/huggingface_hub/v0.22.2/en/guides/inference
-"""
-client = InferenceClient("HuggingFaceH4/zephyr-7b-beta")
-
-
-def respond(
-    message,
-    history: list[tuple[str, str]],
-    system_message,
-    max_tokens,
-    temperature,
-    top_p,
-):
-    messages = [{"role": "system", "content": system_message}]
-
-    for val in history:
-        if val[0]:
-            messages.append({"role": "user", "content": val[0]})
-        if val[1]:
-            messages.append({"role": "assistant", "content": val[1]})
-
-    messages.append({"role": "user", "content": message})
-
-    response = ""
-
-    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 information on how to customize the ChatInterface, peruse the gradio docs: https://www.gradio.app/docs/chatinterface
-"""
-demo = gr.ChatInterface(
-    respond,
-    additional_inputs=[
-        gr.Textbox(value="You are a friendly Chatbot.", label="System message"),
-        gr.Slider(minimum=1, maximum=2048, value=512, step=1, label="Max new tokens"),
-        gr.Slider(minimum=0.1, maximum=4.0, value=0.7, step=0.1, label="Temperature"),
-        gr.Slider(
-            minimum=0.1,
-            maximum=1.0,
-            value=0.95,
-            step=0.05,
-            label="Top-p (nucleus sampling)",
-        ),
-    ],
-)
-
-
+from transformers import pipeline
+
+# Load a pre-trained conversational model from Hugging Face
+chatbot_model = pipeline("text-generation", model="microsoft/DialoGPT-medium")
+
+# Function to handle user input and return chatbot response
+def chat_with_zomato(user_input):
+    # Get the response from the chatbot model
+    response = chatbot_model(user_input, max_length=100, num_return_sequences=1)[0]["generated_text"]
+    return response[len(user_input):].strip()  # Remove the user input part from the response
+
+# Gradio Interface for the chatbot
+def launch_zomato_chatbot():
+    # Define the Gradio interface
+    chatbot_interface = gr.Interface(
+        fn=chat_with_zomato,
+        inputs=gr.Textbox(lines=2, placeholder="Ask Zomato..."),  # User input field
+        outputs="text",  # Text output
+        title="Zomato Chatbot",
+        description="Ask Zomato anything about restaurants, food orders, or cuisine recommendations."
+    )
+    
+    # Launch the interface
+    chatbot_interface.launch()
+
+# Call the function to launch the chatbot
 if __name__ == "__main__":
-    demo.launch()
+    launch_zomato_chatbot()