Update app.py

app.py

@@ -1,143 +1,34 @@
 import gradio as gr
-from sentence_transformers import SentenceTransformer, util
-import openai
-import os
 
-os.environ["TOKENIZERS_PARALLELISM"] = "false"
-
-# Initialize paths and model identifiers for easy configuration and maintenance
-filename = "output_topic_details.txt"  # Path to the file storing chess-specific details
-retrieval_model_name = 'output/sentence-transformer-finetuned/'
-
-openai.api_key = os.environ["OPENAI_API_KEY"]
-
-system_message = "You are a chess chatbot specialized in providing information on chess rules, strategies, and terminology."
-# Initial system message to set the behavior of the assistant
-messages = [{"role": "system", "content": system_message}]
-
-# Attempt to load the necessary models and provide feedback on success or failure
-try:
-    retrieval_model = SentenceTransformer(retrieval_model_name)
-    print("Models loaded successfully.")
-except Exception as e:
-    print(f"Failed to load models: {e}")
-
-def load_and_preprocess_text(filename):
-    """
-    Load and preprocess text from a file, removing empty lines and stripping whitespace.
-    """
-    try:
-        with open(filename, 'r', encoding='utf-8') as file:
-            segments = [line.strip() for line in file if line.strip()]
-        print("Text loaded and preprocessed successfully.")
-        return segments
-    except Exception as e:
-        print(f"Failed to load or preprocess text: {e}")
-        return []
-
-segments = load_and_preprocess_text(filename)
-
-def find_relevant_segment(user_query, segments):
-    """
-    Find the most relevant text segment for a user's query using cosine similarity among sentence embeddings.
-    This version finds the best match based on the content of the query.
-    """
-    try:
-        # Lowercase the query for better matching
-        lower_query = user_query.lower()
-        
-        # Encode the query and the segments
-        query_embedding = retrieval_model.encode(lower_query)
-        segment_embeddings = retrieval_model.encode(segments)
-        
-        # Compute cosine similarities between the query and the segments
-        similarities = util.pytorch_cos_sim(query_embedding, segment_embeddings)[0]
-        
-        # Find the index of the most similar segment
-        best_idx = similarities.argmax()
-        
-        # Return the most relevant segment
-        return segments[best_idx]
-    except Exception as e:
-        print(f"Error in finding relevant segment: {e}")
-        return ""
-
-def generate_response(user_query, relevant_segment):
-    """
-    Generate a response emphasizing the bot's capability in providing chess information.
-    """
-    try:
-        
-        user_message = f"Here's the information on chess: {relevant_segment}"
-
-        # Append user's message to messages list
-        messages.append({"role": "user", "content": user_message})
-
-        response = openai.ChatCompletion.create(
-            model="gpt-3.5-turbo",
-            messages=messages,
-            max_tokens=150,
-            temperature=0.2,
-            top_p=1,
-            frequency_penalty=0,
-            presence_penalty=0
-        )
-        
-        # Extract the response text
-        output_text = response['choices'][0]['message']['content'].strip()
-        
-        # Append assistant's message to messages list for context
-        messages.append({"role": "assistant", "content": output_text})
-        
-        return output_text
-        
-    except Exception as e:
-        print(f"Error in generating response: {e}")
-        return f"Error in generating response: {e}"
-
-def query_model(question):
-    """
-    Process a question, find relevant information, and generate a response.
-    """
-    if question == "":
-        return "Welcome to ChessBot! Ask me anything about chess rules, strategies, and terminology."
-    relevant_segment = find_relevant_segment(question, segments)
-    if not relevant_segment:
-        return "Could not find specific information. Please refine your question."
-    response = generate_response(question, relevant_segment)
-    return response
-
-# Define the welcome message and specific topics the chatbot can provide information about
-welcome_message = """
-# ♟️ Welcome to ChessBot!
-
-## Your AI-driven assistant for all chess-related queries. Created by SCHOLAR1, SCHOLAR2, and SCHOLAR3 of the 2024 Kode With Klossy CITY Camp. 
-"""
-
-topics = """
-### Feel Free to ask me anything from the topics below!
-- Chess piece movements
-- Special moves
-- Game phases
-- Common strategies
-- Chess terminology
-- Famous games
-- Chess tactics
-"""
-
-# Setup the Gradio Blocks interface with custom layout components
-with gr.Blocks(theme='JohnSmith9982/small_and_pretty') as demo:
-    gr.Markdown(welcome_message)  # Display the formatted welcome message
-    with gr.Row():
-        with gr.Column():
-            gr.Markdown(topics)  # Show the topics on the left side
-    with gr.Row():
-        with gr.Column():
-            question = gr.Textbox(label="Your question", placeholder="What do you want to ask about?")
-            answer = gr.Textbox(label="ChessBot Response", placeholder="ChessBot will respond here...", interactive=False, lines=10)
-            submit_button = gr.Button("Submit")
-            submit_button.click(fn=query_model, inputs=question, outputs=answer)
-    
-
-# Launch the Gradio app to allow user interaction
-demo.launch(share=True)
+def yes_man(message, history):
+    if message.endswith("?"):
+        return "Yes"
+    else:
+        return "Ask me anything!"
+
+gr.ChatInterface(
+    yes_man,
+    chatbot=gr.Chatbot(height=300),
+    textbox=gr.Textbox(placeholder="Ask me a yes or no question", container=False, scale=7),
+    title="Yes Man",
+    description="Ask Yes Man any question",
+    theme="soft",
+    examples=["Hello", "Am I cool?", "Are tomatoes vegetables?"],
+    cache_examples=True,
+    retry_btn=None,
+    undo_btn="Delete Previous",
+    clear_btn="Clear",
+).launch()
+
+# def load():
+#     return [
+#         ("Here's an audio", gr.Audio("https://github.com/gradio-app/gradio/raw/main/test/test_files/audio_sample.wav")),
+#         ("Here's an video", gr.Video("https://github.com/gradio-app/gradio/raw/main/demo/video_component/files/world.mp4"))
+#     ]
+
+# with gr.Blocks() as demo:
+#     chatbot = gr.Chatbot()
+#     button = gr.Button("Load audio and video")
+#     button.click(load, None, chatbot)
+
+# demo.launch()