push

app.py

@@ -1,64 +1,189 @@
+
+from typing import final
 import gradio as gr
+import os
+import json
+from bs4 import BeautifulSoup
+import requests
 from huggingface_hub import InferenceClient
+from datetime import datetime, timedelta
+import json
+
+
+# Define global variables
+BOT_AVATAR = 'https://automatedstockmining.org/wp-content/uploads/2024/08/south-west-value-mining-logo.webp'
+client = InferenceClient(token=os.getenv("HF_TOKEN"))
+custom_css = '''
+.gradio-container {
+    font-family: 'Roboto', sans-serif;
+}
+
+.main-header {
+    text-align: center;
+    color: #4a4a4a;
+    margin-bottom: 2rem;
+}
+
+.tab-header {
+    font-size: 1.2rem;
+    font-weight: bold;
+    margin-bottom: 1rem;
+}
+
+.custom-chatbot {
+    border-radius: 10px;
+    box-shadow: 0 4px 6px rgba(0, 0, 0, 0.1);
+}
+
+.custom-button {
+    background-color: #3498db;
+    color: white;
+    border: none;
+    padding: 10px 20px;
+    border-radius: 5px;
+    cursor: pointer;
+    transition: background-color 0.3s ease;
+}
+
+.custom-button:hover {
+    background-color: #2980b9;
+}
+'''
+
+
+def extract_text_from_webpage(html):
+    soup = BeautifulSoup(html, "html.parser")
+    # Extract visible text, removing unnecessary elements (e.g., scripts, styles)
+    for script in soup(["script", "style"]):
+        script.decompose()
+    visible_text = soup.get_text(separator=" ", strip=True)
+    return visible_text
+
+def search(query):
+    term = query
+    max_chars_per_page = 8000
+    all_results = []
+
+    with requests.Session() as session:
+        try:
+            # Send a search request to Google
+            resp = session.get(
+                url="https://www.google.com/search",
+                headers={"User-Agent": "Mozilla/5.0 (Windows NT 10.0; Win64; x64; rv:109.0) Gecko/20100101 Firefox/111.0"},
+                params={"q": term, "num": 4},
+                timeout=5
+            )
+            resp.raise_for_status()  # Ensure the request was successful
+
+            soup = BeautifulSoup(resp.text, "html.parser")
+            result_block = soup.find_all("div", attrs={"class": "g"})
+
+            for result in result_block:
+                link = result.find("a", href=True)
+                if link:
+                    link = link["href"]
+                    try:
+                        # Fetch the webpage at the found link
+                        webpage = session.get(link, headers={"User-Agent": "Mozilla/5.0"}, timeout=5)
+                        webpage.raise_for_status()
+
+                        # Extract visible text from the webpage
+                        visible_text = extract_text_from_webpage(webpage.text)
+                        if len(visible_text) > max_chars_per_page:
+                            visible_text = visible_text[:max_chars_per_page]
+
+                        all_results.append({"link": link, "text": visible_text})
+
+                    except requests.exceptions.RequestException as e:
+                        print(f"Failed to retrieve {link}: {e}")
+                        all_results.append({"link": link, "text": None})
+        except requests.exceptions.RequestException as e:
+            print(f"Google search failed: {e}")
+
+    return all_results
+def process_query(user_input, history):
+  if len(history) > 0 and history[-1]['role'] == 'user' and history[-1]['content'].lower() == user_input.lower():
+        gr.Info('Searching the web for the latest data...', duration=4)
+  else:
+      # Append new user message to the history
+      history.append({"role": "user", "content": user_input})
+
+  search_results = search(user_input)
+  search_results_str = json.dumps(search_results)
+  response = client.chat_completion(
+              model="Qwen/Qwen2.5-72B-Instruct",
+              messages=[{"role": "user", "content": f"YOU ARE IM.X, AN INVESTMENT CHATBOT BUILT BY automatedstockmining.org. Answer the user's request '{user_input}' using the following information: {search_results_str} and the chat history{history}. Provide a concise, direct answer in no more than 2-3 sentences. use the appropriate emojis for some of your responses"}],
+              max_tokens=400,
+              stream=False
+          )
+  final_response = response.choices[0].message['content']
+  history.append({"role": "assistant", "content": final_response})
 
-"""
-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)",
-        ),
-    ],
+  return history, ""  # Clear the input box after sending the response
+
+
+def clear_history():
+    return [], ""  # Return empty history and clear the input box
+
+# Function to undo the last user-bot message pair
+def undo_last(history):
+    if len(history) >= 2:  # Ensure that there's at least one user-bot message pair
+        history.pop()  # Remove the bot's response
+        history.pop()  # Remove the user's input
+    return history, ""  # Return updated history and clear the input box
+
+# Gradio UI setup
+
+
+theme = gr.themes.Citrus(
+    primary_hue="blue",
+    neutral_hue="slate",
 )
 
 
-if __name__ == "__main__":
-    demo.launch()
+with gr.Blocks(theme = theme,css = custom_css) as demo:
+    gr.Markdown("<h1 style='text-align: center;'>IM.B - Intelligent Investing with automatedstockmining.org</h1>")
+
+    with gr.Column():
+        chatbot_display = gr.Chatbot(label="IM.B chat", avatar_images=[None, BOT_AVATAR], height=600, type="messages", layout="panel")
+
+        # User input and send button
+        with gr.Row():
+            user_input = gr.Textbox(placeholder="Type your message here...", label=None, show_label=False)
+            send_button = gr.Button("Send")
+
+        example_inputs = [
+            ["What's the current price of bitcoin"],
+            ["What's the latest news on Cisco Systems stock"],
+            ["Analyze technical indicators for Adobe, are they presenting buy or sell signals"],
+            ["What's the current price of Apple stock"],
+            ["What are the best stocks to buy this month"],
+            ['what companies report earnings this week'],
+            ["whats apple's current market cap"]
+        ]
+
+        # Add examples to the UI
+        gr.Examples(examples=example_inputs, inputs=user_input)
+
+        # Buttons for Clear and Undo
+        with gr.Row():
+            clear_button = gr.Button("Clear Chat")
+            undo_button = gr.Button("Undo Last")
+
+    # History is now handled as part of the chatbot_display
+    send_button.click(process_query, inputs=[user_input, chatbot_display], outputs=[chatbot_display, user_input])
+
+    # Allow pressing Enter to submit the input
+    user_input.submit(process_query, inputs=[user_input, chatbot_display], outputs=[chatbot_display, user_input])
+
+    # Action for Clear Button
+    clear_button.click(clear_history, outputs=[chatbot_display, user_input])
+
+    # Action for Undo Button
+    undo_button.click(undo_last, inputs=[chatbot_display], outputs=[chatbot_display, user_input])
+
+# Launch the Gradio app
+demo.launch()
+
+
+