Create app.py

app.py

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+import torch
+from transformers import BertTokenizer, BertModel, GPT2LMHeadModel, GPT2Tokenizer
+import numpy as np
+import pandas as pd
+import os
+import json
+from fastapi import FastAPI
+from pydantic import BaseModel
+
+app = FastAPI()
+
+data = {
+    "questions": [
+        "What is Rookus?",
+        "How does Rookus use AI in its designs?",
+        "What products does Rookus offer?",
+        "Can I see samples of Rookus' designs?",
+        "How can I join the waitlist for Rookus?",
+        "How does Rookus ensure the quality of its AI-generated designs?",
+        "Is there a custom design option available at Rookus?",
+        "How long does it take to receive a product from Rookus?"
+    ],
+    "answers": [
+        "Rookus is a startup that leverages AI to create unique designs for various products such as clothes, posters, and different arts and crafts.",
+        "Rookus uses advanced AI algorithms to generate innovative and aesthetically pleasing designs. These AI models are trained on vast datasets of art and design to produce high-quality mockups.",
+        "Rookus offers a variety of products, including clothing, posters, and a range of arts and crafts items, all featuring AI-generated designs.",
+        "Yes, Rookus provides samples of its designs on its website. You can view a gallery of products showcasing the AI-generated artwork.",
+        "To join the waitlist for Rookus, visit our website and sign up with your email. You'll receive updates on our launch and exclusive early access opportunities.",
+        "Rookus ensures the quality of its AI-generated designs through rigorous testing and refinement. Each design goes through multiple review stages to ensure it meets our high standards.",
+        "Yes, Rookus offers custom design options. You can submit your preferences, and our AI will generate a design tailored to your specifications.",
+        "The delivery time for products from Rookus varies based on the product type and location. Typically, it takes 2-4 weeks for production and delivery."
+    ],
+    "default_answers": "I'm sorry, I cannot answer this right now. Your question has been saved, and we will get back to you with a response soon."
+}
+
+bert_model_name = 'models/bert'
+bert_tokenizer = BertTokenizer.from_pretrained(bert_model_name)
+bert_model = BertModel.from_pretrained(bert_model_name)
+
+gpt2_model_name = 'models/gpt2'
+gpt2_tokenizer = GPT2Tokenizer.from_pretrained(gpt2_model_name)
+gpt2_model = GPT2LMHeadModel.from_pretrained(gpt2_model_name)
+
+def get_bert_embeddings(texts):
+    inputs = bert_tokenizer(texts, return_tensors='pt', padding=True, truncation=True)
+    with torch.no_grad():
+        outputs = bert_model(**inputs)
+    return outputs.last_hidden_state[:, 0, :].numpy()
+
+def get_closest_question(user_query, questions, threshold=0.95):
+    all_texts = questions + [user_query]
+    embeddings = get_bert_embeddings(all_texts)
+    cosine_similarities = np.dot(embeddings[-1], embeddings[:-1].T) / (
+        np.linalg.norm(embeddings[-1]) * np.linalg.norm(embeddings[:-1], axis=1)
+    )
+    max_similarity = np.max(cosine_similarities)
+    
+    if max_similarity >= threshold:
+        most_similar_index = np.argmax(cosine_similarities)
+        return questions[most_similar_index], max_similarity
+    else:
+        return None, max_similarity
+
+def generate_gpt2_response(prompt, model, tokenizer, max_length=100):
+    inputs = tokenizer.encode(prompt, return_tensors='pt')
+    outputs = model.generate(inputs, max_length=max_length, num_return_sequences=1)
+    return tokenizer.decode(outputs[0], skip_special_tokens=True)
+
+class QueryRequest(BaseModel):
+    query: str
+
+@app.post("/query/")
+def answer_query(request: QueryRequest):
+    user_query = request.query
+    closest_question, similarity = get_closest_question(user_query, data['questions'], threshold=0.95)
+    if closest_question and similarity >= 0.95: 
+        answer_index = data['questions'].index(closest_question)
+        answer = data['answers'][answer_index]
+    else:
+        excel_file = 'new_questions1.xlsx'
+        if not os.path.isfile(excel_file):
+            df = pd.DataFrame(columns=['question'])
+            df.to_excel(excel_file, index=False)
+        
+        new_data = pd.DataFrame({'questions': [user_query]})
+        df = pd.read_excel(excel_file)
+        df = pd.concat([df, new_data], ignore_index=True)
+        with pd.ExcelWriter(excel_file, engine='openpyxl', mode='w') as writer:
+            df.to_excel(writer, index=False)
+        answer = data['default_answers']
+
+    return {"query": user_query, "answer": answer}
+
+if __name__ == "__main__":
+    import uvicorn
+    uvicorn.run(app, host="0.0.0.0", port=8000)