Update app.py

app.py

@@ -3,8 +3,8 @@ from dotenv import find_dotenv, load_dotenv
 import streamlit as st
 from typing import Generator
 from groq import Groq
-import requests
-from bs4 import BeautifulSoup
+import datetime
+import json
 
 _ = load_dotenv(find_dotenv())
 st.set_page_config(page_icon="💬", layout="wide", page_title="Groq Chat Bot...")
@@ -16,24 +16,22 @@ def icon(emoji: str):
         unsafe_allow_html=True,
     )
 
-icon("⚡")
+icon("📣")
 
-st.subheader("GroqChatbot", divider="rainbow", anchor=False)
+st.subheader("Groq Chat Streamlit App", divider="rainbow", anchor=False)
 
-client = Groq(api_key=os.environ['GROQ_API_KEY'])
-
-if "messages" not in st.session_state:
-    st.session_state.messages = []
-
-if "selected_model" not in st.session_state:
-    st.session_state.selected_model = None
+client = Groq(
+    api_key=os.environ['GROQ_API_KEY'],
+)
 
 models = {
-    "mixtral-8x7b-32768": {"name": "Mixtral-8x7b-Instruct-v0.1", "tokens": 32768, "developer": "Mistral"},
-    "gemma-7b-it": {"name": "Gemma-7b-it", "tokens": 8192, "developer": "Google"},
+    "mixtral-8x7b-32768": {
+        "name": "Mixtral-8x7b-Instruct-v0.1",
+        "tokens": 32768,
+        "developer": "Mistral",
+    },
     "llama2-70b-4096": {"name": "LLaMA2-70b-chat", "tokens": 4096, "developer": "Meta"},
-    "llama3-70b-8192": {"name": "LLaMA3-70b-8192", "tokens": 8192, "developer": "Meta"},
-    "llama3-8b-8192": {"name": "LLaMA3-8b-8192", "tokens": 8192, "developer": "Meta"},
+    "gemma-7b-it": {"name": "Gemma-7b-it", "tokens": 8192, "developer": "Google"},
 }
 
 col1, col2 = st.columns(2)
@@ -43,9 +41,15 @@ with col1:
         "Choose a model:",
         options=list(models.keys()),
         format_func=lambda x: models[x]["name"],
-        index=0, 
+        index=0,
     )
 
+if "messages" not in st.session_state:
+    st.session_state.messages = []
+
+if "selected_model" not in st.session_state:
+    st.session_state.selected_model = None
+
 if st.session_state.selected_model != model_option:
     st.session_state.messages = []
     st.session_state.selected_model = model_option
@@ -55,7 +59,7 @@ max_tokens_range = models[model_option]["tokens"]
 with col2:
     max_tokens = st.slider(
         "Max Tokens:",
-        min_value=512,  
+        min_value=512,
         max_value=max_tokens_range,
         value=min(32768, max_tokens_range),
         step=512,
@@ -72,57 +76,80 @@ def generate_chat_responses(chat_completion) -> Generator[str, None, None]:
     for chunk in chat_completion:
         if chunk.choices[0].delta.content:
             yield chunk.choices[0].delta.content
+        if chunk.choices[0].message.tool_calls:
+            for tool_call in chunk.choices[0].message.tool_calls:
+                function_name = tool_call.function.name
+                if function_name == "time_date":
+                    owner_info = get_tool_owner_info()
+                    yield owner_info
+
+def run_conversation(user_prompt):
+    messages=[
+        {
+            "role": "system",
+            "content": "You are a helpful assistant named ChattyBot."
+        },
+        {
+            "role": "user",
+            "content": user_prompt,
+        }
+    ]
+    tools = [
+        {
+            "type": "function",
+            "function": {
+                "name": "time_date",
+                "description": "The tool will return information about the time and date to the AI.",
+                "parameters": {},
+            },
+        }
+    ]
+    response = client.chat.completions.create(
+        model=model_option,
+        messages=messages,
+        tools=tools,
+        tool_choice="auto",
+        max_tokens=4096
+    )
 
-def search_web(query):
-    result = {"query": query, "data": {}}
-    try:
-        search_url = f"https://www.google.com/search?q={query}"
-        response = requests.get(search_url)
-        if response.status_code == 200:
-            soup = BeautifulSoup(response.text, 'html.parser')
-
-            # Scrape organic search results
-            result["data"]["organic"] = []
-            results = soup.find_all('div', class_='g')
-            for result in results:
-                a_tag = result.find('a')
-                if a_tag is not None and 'title' in a_tag.attrs:
-                    title = a_tag['title']
-                else:
-                    title = ''
-                url = a_tag['href'] if 'href' in a_tag.attrs else ''
-                snippet = result.find('span', class_='aCOpRe')
-                if snippet is not None:
-                    snippet = snippet.text
-                else:
-                    snippet = ''
-                item = {"title": title, "url": url, "snippet": snippet}
-                result["data"]["organic"].append(item)
-
-            # Scrape knowledge panel
-            result["data"]["knowledge_panel"] = {}
-            knowledge_panel = soup.find('div', id='knowledge-kp')
-            if knowledge_panel is not None:
-                title = knowledge_panel.find('h3').text
-                content = knowledge_panel.find('div', class_='VwiC3b').text
-                result["data"]["knowledge_panel"]["title"] = title
-                result["data"]["knowledge_panel"]["content"] = content
-
-            # Scrape images
-            result["data"]["images"] = []
-            images = soup.find_all('div', class_='hdtb-mitem hdtb-msel')
-            for image in images:
-                a_tag = image.find('a')
-                title = a_tag['title'] if 'title' in a_tag.attrs else ''
-                url = a_tag['href'] if 'href' in a_tag.attrs else ''
-                item = {"title": title, "url": url, "snippet": ''}
-                result["data"]["images"].append(item)
-        else:
-            result["error"] = "Failed to retrieve search results"
-    except Exception as e:
-        result["error"] = f"An error occurred: {e}"
-    return result
-full_response = None  # Initialize full_response to None
+    response_message = response.choices[0].message
+    tool_calls = response_message.tool_calls
+
+    if tool_calls:
+        available_functions = {
+            "time_date": get_tool_owner_info
+        }
+
+        messages.append(response_message)
+
+        for tool_call in tool_calls:
+            function_name = tool_call.function.name
+            function_to_call = available_functions[function_name]
+            function_args = json.loads(tool_call.function.arguments)
+            function_response = function_to_call(**function_args)
+            messages.append(
+                {
+                    "tool_call_id": tool_call.id,
+                    "role": "tool",
+                    "name": function_name,
+                    "content": function_response,
+                }
+            )
+
+        second_response = client.chat.completions.create(
+            model=model_option,
+            messages=messages
+        )
+
+        return second_response.choices[0].message.content
+    else:
+        return response_message.content
+
+def get_tool_owner_info():
+    owner_info = {
+        "date_time": datetime.datetime.now().strftime("%Y-%m-%d %H:%M:%S")
+    }
+    return json.dumps(owner_info)
 
 if prompt := st.chat_input("Enter your prompt here..."):
     st.session_state.messages.append({"role": "user", "content": prompt})
@@ -131,38 +158,28 @@ if prompt := st.chat_input("Enter your prompt here..."):
         st.markdown(prompt)
 
     try:
-        if "search for" in prompt.lower():
-            query = prompt.lower().replace("search for", "").strip()
-            search_results = search_web(query)
-            formatted_results = "\n\n".join([f"Title: {result['title']}\nURL: {result['url']}\nSnippet: {result['snippet']}" for result in search_results])
-            st.session_state.messages.append({"role": "assistant", "content": formatted_results})
-            with st.chat_message("assistant", avatar="🤖"):
-               full_response = formatted_results
-        else:
-            chat_completion = client.chat.completions.create(
-                model=model_option,
-                messages=[
-                    {"role": m["role"], "content": m["content"]}
-                    for m in st.session_state.messages
-                ],
-                max_tokens=max_tokens,
-                stream=True,
-            )
-
-            with st.chat_message("assistant", avatar="🤖"):
-                chat_responses_generator = generate_chat_responses(chat_completion)
-                full_response = st.write_stream(chat_responses_generator)
+        chat_completion = client.chat.completions.create(
+            model=model_option,
+            messages=[
+                {"role": m["role"], "content": m["content"]}
+                for m in st.session_state.messages
+            ],
+            max_tokens=max_tokens,
+            stream=True,
+        )
+
+        with st.chat_message("assistant", avatar="🤖"):
+            chat_responses_generator = generate_chat_responses(chat_completion)
+            full_response = st.write_stream(chat_responses_generator)
     except Exception as e:
         st.error(e, icon="🚨")
 
-    # Check if full_response is defined before using it
-    if full_response is not None:
-        if isinstance(full_response, str):
-            st.session_state.messages.append(
-                {"role": "assistant", "content": full_response}
-            )
-        else:
-            combined_response = "\n".join(str(item) for item in full_response)
-            st.session_state.messages.append(
-                {"role": "assistant", "content": combined_response}
-            )
+    if isinstance(full_response, str):
+        st.session_state.messages.append(
+            {"role": "assistant", "content": full_response}
+        )
+    else:
+        combined_response = "\n".join(str(item) for item in full_response)
+        st.session_state.messages.append(
+            {"role": "assistant", "content": combined_response}
+        )