Update app.py

app.py

@@ -1,8 +1,47 @@
-from g4f.client import Client
+import gradio as gr
+from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline
 
-client = Client()
-response = client.chat.completions.create(
-    model="gpt-3.5-turbo",
-    messages=[{"role": "user", "content": "Hello"}],
+# Load the model and tokenizer from Hugging Face
+model_name = "OpenAssistant/oasst-sft-4-pythia-12b-epoch-3.5"  # Example model, replace with the desired model
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+model = AutoModelForCausalLM.from_pretrained(model_name)
+
+# Create a pipeline for text generation
+chat_pipeline = pipeline("text-generation", model=model, tokenizer=tokenizer)
+
+# Define the function that handles chat interaction
+def chat_with_model(user_input, history=[]):
+    # Append the user input to the history
+    history.append({"role": "user", "content": user_input})
+
+    # Prepare the prompt from the history
+    prompt = "\n".join([f"{item['role']}: {item['content']}" for item in history])
+
+    # Generate a response
+    response = chat_pipeline(prompt, max_length=100, num_return_sequences=1)[0]['generated_text']
+
+    # Extract only the model's response
+    model_response = response[len(prompt):].strip()
+
+    # Append model response to history
+    history.append({"role": "assistant", "content": model_response})
+
+    return model_response, history
+
+# Define the Gradio interface
+iface = gr.Interface(
+    fn=chat_with_model,
+    inputs=[
+        gr.inputs.Textbox(label="User Input"),
+        gr.inputs.State(),
+    ],
+    outputs=[
+        gr.outputs.Textbox(label="Chatbot Response"),
+        gr.outputs.State(),
+    ],
+    title="HuggingChat Clone",
+    description="A simple chatbot interface using an open-source model from Hugging Face."
 )
-print(response.choices[0].message.content)
+
+# Launch the interface
+iface.launch(server_name="0.0.0.0", server_port=7860)