Update app.py

app.py

@@ -1,72 +1,166 @@
-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,
+import streamlit as st
+from openai import OpenAI
+import os
+import numpy as np
+from dotenv import load_dotenv
+import openai
+
+# Load environment variables
+load_dotenv()
+
+# Initialize the OpenAI client for OpenAI models
+openai.api_key = os.getenv("OPENAI_API_KEY")
+
+# Hugging Face API client setup (if needed)
+HF_API_KEY = os.getenv("HF_API_KEY")
+huggingface_url = "https://api-inference.huggingface.co/models/"
+
+# Create supported models dictionary
+model_links = {
+    "ChatGPT": "openai/gpt-4",
+    "Meta-Llama-3.1-70B-Instruct": "meta-llama/Meta-Llama-3.1-70B-Instruct",
+    "Mistral-7B-Instruct-v0.1": "mistralai/Mistral-7B-Instruct-v0.1",
+    # Add more models as needed
+}
+
+# Define functions to interact with OpenAI and Hugging Face
+def query_openai(prompt, temperature):
+    """Query OpenAI's GPT model."""
+    response = openai.ChatCompletion.create(
+        model="gpt-4",
+        messages=[{"role": "user", "content": prompt}],
         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)",
-        ),
-    ],
-)
-
-
-if __name__ == "__main__":
-    demo.launch()
+    )
+    return response.choices[0].message['content']
+
+def query_huggingface(prompt, model, temperature):
+    """Query Hugging Face's API."""
+    headers = {"Authorization": f"Bearer {HF_API_KEY}"}
+    payload = {
+        "inputs": prompt,
+        "parameters": {"temperature": temperature, "return_full_text": False},
+    }
+    response = requests.post(f"{huggingface_url}{model}", headers=headers, json=payload)
+    return response.json()[0]['generated_text']
+
+# Function to reset conversation
+def reset_conversation():
+    st.session_state.messages = []
+    st.session_state.responses = []
+    st.session_state.current_model = None
+
+# Sidebar setup
+st.sidebar.title("ChatBot Configuration")
+selected_model = st.sidebar.selectbox("Select Model", list(model_links.keys()))
+temperature = st.sidebar.slider("Temperature", 0.0, 1.0, 0.5)
+
+# Reset chat button
+st.sidebar.button('Reset Chat', on_click=reset_conversation)
+
+# Initialize session state variables
+if 'messages' not in st.session_state:
+    st.session_state.messages = []
+if 'responses' not in st.session_state:
+    st.session_state.responses = []
+if 'current_model' not in st.session_state:
+    st.session_state.current_model = selected_model
+
+# Check if the model was changed
+if st.session_state.current_model != selected_model:
+    reset_conversation()
+    st.session_state.current_model = selected_model
+
+# Chat Interface
+st.title(f"Chat with {selected_model}")
+
+# Display previous chat messages
+for message in st.session_state.messages:
+    with st.chat_message(message["role"]):
+        st.markdown(message["content"])
+
+# Accept user input
+if prompt := st.chat_input("Ask me anything..."):
+    st.session_state.messages.append({"role": "user", "content": prompt})
+
+    with st.chat_message("assistant"):
+        if selected_model == "ChatGPT":
+            response = query_openai(prompt, temperature)
+        else:
+            response = query_huggingface(prompt, model_links[selected_model], temperature)
+        st.markdown(response)
+
+    st.session_state.responses.append(response)
+    st.session_state.messages.append({"role": "assistant", "content": response})
 
 
+# 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)",
+#         ),
+#     ],
+# )
+
+
+# if __name__ == "__main__":
+#     demo.launch()
+#####################################
 # import gradio as gr
 
 # gr.load("models/meta-llama/Meta-Llama-3.1-70B-Instruct").launch()
-
+########################################
 # import streamlit as st
 # from transformers import AutoTokenizer, AutoModelForCausalLM