Create app.py

Hugchat x Metaphor/app.py

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+import streamlit as st
+from hugchat import hugchat
+from hugchat.login import Login
+from metaphor_python import Metaphor
+
+
+# App title
+st.set_page_config(page_title="HugChat with Metaphor")
+
+# Define Metaphor API key
+METAPHOR_API_KEY = "1cd6d71b-e530-4ea3-bb18-e9599e641f66"  # Replace with your Metaphor API key
+with st.sidebar:
+    st.title('🤗💬 HugChat x Metaphor')
+    if ('EMAIL' in st.secrets) and ('PASS' in st.secrets):
+        st.success('HuggingFace Login credentials already provided!', icon='✅')
+        hf_email = st.secrets['EMAIL']
+        hf_pass = st.secrets['PASS']
+    else:
+        hf_email = st.text_input('Enter E-mail:', type='password')
+        hf_pass = st.text_input('Enter password:', type='password')
+        if not (hf_email and hf_pass):
+            st.warning('Please enter your credentials!', icon='⚠️')
+        else:
+            st.success('Proceed to entering your prompt message!', icon='👉')
+
+# Create Metaphor client
+metaphor = Metaphor(METAPHOR_API_KEY)
+
+# Store LLM generated responses
+if "messages" not in st.session_state:
+    st.session_state.messages = [{"role": "assistant", "content": "Heya Metaphor bot this side, how may i assist ?"}]
+
+# Display or clear chat messages
+for message in st.session_state.messages:
+    with st.chat_message(message["role"]):
+        st.write(message["content"])
+
+def clear_chat_history():
+    st.session_state.messages = [{"role": "assistant", "content": "Heya Metaphor bot this side, how may i assist?"}]
+st.sidebar.button('Clear Chat History', on_click=clear_chat_history)
+
+# Function for generating LLM response
+def generate_response(prompt_input, email, passwd):
+    # Hugging Face Login
+    sign = Login(email, passwd)
+    cookies = sign.login()
+
+    # Create ChatBot
+    chatbot = hugchat.ChatBot(cookies=cookies.get_dict())
+
+    # Check if the user's input is a specific question
+    if prompt_input.strip().lower() in ["who are you?", "who made you?"]:
+        response = "I am an AI LLama Hugchat of Huggingface  which is integrated with Metaphor in the backend."
+    else:
+        # Fetch Metaphor search results
+        search_options = {
+            "query": prompt_input,
+            "num_results": 5  # You can adjust the number of results as needed
+        }
+        try:
+            search_response = metaphor.search(**search_options)
+
+            # Extract links and summaries from the Metaphor search results
+            links_and_summaries = [
+                f"Title: {result.title}\nURL: {result.url}\nSummary: {result.extract}\n---"
+                for result in search_response.results
+            ]
+
+            # Combine the user's query and Metaphor output with the previous conversation
+            string_dialogue = "You are a helpful assistant."
+            for dict_message in st.session_state.messages:
+                if dict_message["role"] == "user":
+                    string_dialogue += "User: " + dict_message["content"] + "\n\n"
+                else:
+                    string_dialogue += "Assistant: " + dict_message["content"] + "\n\n"
+
+            prompt = f"{string_dialogue}\n{prompt_input}\n{''.join(links_and_summaries)}\n Assistant: "
+            response = chatbot.chat(prompt)
+        except Exception as e:
+            response = str(e)
+
+    return response
+# User-provided prompt
+if prompt := st.chat_input(disabled=not (hf_email and hf_pass)):
+    st.session_state.messages.append({"role": "user", "content": prompt})
+    with st.chat_message("user"):
+        st.write(prompt)
+
+    # Generate a new response if the last message is not from the assistant
+    if st.session_state.messages[-1]["role"] != "assistant":
+        with st.chat_message("assistant"):
+            with st.spinner("Thinking..."):
+                response = generate_response(prompt, hf_email, hf_pass)
+                st.write(response)
+
+        message = {"role": "assistant", "content": response}
+        st.session_state.messages.append(message)