Create app.py

app.py

@@ -0,0 +1,125 @@
+import os
+import sqlite3
+import requests
+import PyPDF2
+import faiss
+import numpy as np
+from sentence_transformers import SentenceTransformer
+import gradio as gr
+
+# Configure Hugging Face API
+huggingface_api_url = "https://api-inference.huggingface.co/models/meta-llama/Meta-Llama-3-70B-Instruct"
+huggingface_api_key = os.getenv("HUGGINGFACE_API_KEY")
+headers = {"Authorization": f"Bearer {huggingface_api_key}"}
+
+# Function to query Hugging Face model
+def query_huggingface(payload):
+    response = requests.post(huggingface_api_url, headers=headers, json=payload)
+    return response.json()
+
+# Function to extract text from PDF
+def extract_text_from_pdf(pdf_file):
+    pdf_reader = PyPDF2.PdfReader(pdf_file)
+    text = ""
+    for page_num in range(len(pdf_reader.pages)):
+        page = pdf_reader.pages[page_num]
+        text += page.extract_text()
+    return text
+
+# Initialize SQLite database
+def init_db():
+    conn = sqlite3.connect('storage_warehouse.db')
+    c = conn.cursor()
+    c.execute('''
+        CREATE TABLE IF NOT EXISTS context (
+            id INTEGER PRIMARY KEY AUTOINCREMENT,
+            name TEXT,
+            content TEXT
+        )
+    ''')
+    conn.commit()
+    conn.close()
+
+# Add context to the database
+def add_context(name, content):
+    conn = sqlite3.connect('storage_warehouse.db')
+    c = conn.cursor()
+    c.execute('INSERT INTO context (name, content) VALUES (?, ?)', (name, content))
+    conn.commit()
+    conn.close()
+
+# Retrieve context from the database
+def get_context():
+    conn = sqlite3.connect('storage_warehouse.db')
+    c = conn.cursor()
+    c.execute('SELECT content FROM context')
+    context = c.fetchall()
+    conn.close()
+    return [c[0] for c in context]
+
+# Function to create or update the FAISS index
+def update_faiss_index():
+    contexts = get_context()
+    embeddings = model.encode(contexts, convert_to_tensor=True)
+    index = faiss.IndexFlatL2(embeddings.shape[1])
+    index.add(embeddings.cpu().numpy())
+    return index, contexts
+
+# Retrieve relevant context from the FAISS index
+def retrieve_relevant_context(index, contexts, query, top_k=5):
+    query_embedding = model.encode([query], convert_to_tensor=True).cpu().numpy()
+    distances, indices = index.search(query_embedding, top_k)
+    relevant_contexts = [contexts[i] for i in indices[0]]
+    return relevant_contexts
+
+# Initialize the database and FAISS model
+init_db()
+model = SentenceTransformer('all-MiniLM-L6-v2')
+faiss_index, context_list = update_faiss_index()
+
+# Function to handle chatbot responses
+def chatbot_response(question):
+    relevant_contexts = retrieve_relevant_context(faiss_index, context_list, question)
+    user_input = f"question: {question} context: {' '.join(relevant_contexts)}"
+    response = query_huggingface({"inputs": user_input})
+    response_text = response.get("generated_text", "Sorry, I couldn't generate a response.")
+    return response_text
+
+# Function to handle PDF uploads
+def handle_pdf_upload(pdf_file):
+    context = extract_text_from_pdf(pdf_file)
+    add_context(pdf_file.name, context)
+    faiss_index, context_list = update_faiss_index()  # Update FAISS index
+    return f"Context from {pdf_file.name} added to the database."
+
+# Gradio UI
+with gr.Blocks() as demo:
+    gr.Markdown("# Storage Warehouse Customer Service Chatbot")
+
+    with gr.Row():
+        with gr.Column(scale=4):
+            with gr.Box():
+                pdf_upload = gr.File(label="Upload PDF", file_types=["pdf"], interactive=True)
+                upload_button = gr.Button("Upload")
+                upload_status = gr.Textbox(label="Upload Status")
+
+            def handle_upload(files):
+                for file in files:
+                    result = handle_pdf_upload(file.name)
+                    upload_status.value = result
+
+            upload_button.click(fn=handle_upload, inputs=pdf_upload, outputs=upload_status)
+
+        with gr.Column(scale=8):
+            chatbot = gr.Chatbot(label="Chatbot")
+            question = gr.Textbox(label="Your question here:")
+            submit_button = gr.Button("Submit")
+
+            def handle_chat(user_input):
+                bot_response = chatbot_response(user_input)
+                return gr.Chatbot.update([[user_input, bot_response]])
+
+            submit_button.click(fn=handle_chat, inputs=question, outputs=chatbot)
+
+if __name__ == "__main__":
+    demo.launch()