Update app.py

app.py

@@ -1,63 +1,90 @@
 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)",
-        ),
-    ],
-)
+import pandas as pd
+import sqlite3
+import openai
+import pinecone
+import chromadb
+from PyPDF2 import PdfReader
+from transformers import pipeline
+import os
+from google.colab import auth
+from googleapiclient.discovery import build
 
+# Initialize APIs
+openai.api_key = "YOUR_OPENAI_API_KEY"
+pinecone.init(api_key="YOUR_PINECONE_API_KEY", environment="us-west1-gcp")
+db = chromadb.Client() # Initialize ChromaDB
+
+# Set up Gradio interface
+def initialize_models(model_choice):
+    if model_choice == 'OpenAI':
+        return {
+            'embedding': openai.Embedding.create,
+            'chat': openai.ChatCompletion.create
+        }
+    elif model_choice == 'HuggingFace':
+        embedding_model = pipeline('feature-extraction', model='bert-base-uncased')
+        chat_model = pipeline('conversational', model='facebook/blenderbot-400M-distill')
+        return {
+            'embedding': embedding_model,
+            'chat': chat_model
+        }
+
+def fetch_pdf_from_drive(file_id):
+    auth.authenticate_user()
+    drive_service = build('drive', 'v3')
+    request = drive_service.files().get_media(fileId=file_id)
+    file = io.BytesIO(request.execute())
+    pdf_reader = PdfReader(file)
+    text = ""
+    for page in pdf_reader.pages:
+        text += page.extract_text()
+    return text
+
+def query_db(query, db_type):
+    conn = sqlite3.connect('data.db')
+    cursor = conn.cursor()
+    if db_type == 'Pinecone':
+        index = pinecone.Index('your-index-name')
+        results = index.query(query)
+        return results
+    elif db_type == 'ChromaDB':
+        # Example of ChromaDB query - adapt as needed
+        results = db.query(query)
+        return results
+    conn.close()
+
+def generate_response(model_choice, query, chat_history, db_type):
+    models = initialize_models(model_choice)
+    if model_choice == 'OpenAI':
+        response = models['chat'](model='gpt-3.5-turbo', messages=chat_history + [{'role': 'user', 'content': query}])
+        return response['choices'][0]['message']['content'], chat_history + [{'role': 'user', 'content': query}, {'role': 'assistant', 'content': response['choices'][0]['message']['content']}]
+    elif model_choice == 'HuggingFace':
+        response = models['chat'](query)
+        return response['generated_text'], chat_history + [{'role': 'user', 'content': query}, {'role': 'assistant', 'content': response['generated_text']}]
+
+def process_input(model_choice, query, db_type, file_id):
+    if file_id:
+        pdf_text = fetch_pdf_from_drive(file_id)
+        query = f"{query} {pdf_text}"
+    response, updated_history = generate_response(model_choice, query, chat_history, db_type)
+    return response, updated_history
+
+def gradio_interface():
+    with gr.Blocks() as demo:
+        with gr.Row():
+            model_choice = gr.Dropdown(['OpenAI', 'HuggingFace'], label='Model Choice', value='OpenAI')
+            db_type = gr.Dropdown(['ChromaDB', 'Pinecone'], label='Database Type', value='ChromaDB')
+            file_id = gr.Textbox(label='Google Drive File ID', placeholder='Enter Google Drive file ID (for PDFs)')
+        
+        with gr.Row():
+            chat_history = gr.Chatbot()
+            query = gr.Textbox(label='Query')
+            submit_button = gr.Button('Submit')
+        
+        submit_button.click(fn=process_input, inputs=[model_choice, query, db_type, file_id], outputs=[chat_history])
+    
+    return demo
 
 if __name__ == "__main__":
-    demo.launch()
+    gradio_interface().launch()