Create app.py

app.py

@@ -0,0 +1,115 @@
+
+from transformers import pipeline, AutoTokenizer, AutoModel
+from torchvision import models, transforms
+from PIL import Image
+import faiss
+
+class TextClassifier:
+    def __init__(self, model_name='distilbert-base-uncased'):
+        self.classifier = pipeline("text-classification", model=model_name)
+
+    def classify(self, text):
+        return self.classifier(text)[0]['label']
+
+
+class SentimentAnalyzer:
+    def __init__(self, model_name='nlptown/bert-base-multilingual-uncased-sentiment'):
+        self.analyzer = pipeline("sentiment-analysis", model=model_name)
+
+    def analyze(self, text):
+        return self.analyzer(text)[0]
+
+
+
+
+class ImageRecognizer:
+    def __init__(self, model_name='resnet50'):
+        self.model = models.resnet50(pretrained=True)
+        self.model.eval()
+        self.transform = transforms.Compose([
+            transforms.Resize(256),
+            transforms.CenterCrop(224),
+            transforms.ToTensor(),
+            transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]),
+        ])
+
+    def recognize(self, image_path):
+        image = Image.open(image_path)
+        image = self.transform(image).unsqueeze(0)
+        with torch.no_grad():
+            outputs = self.model(image)
+        _, predicted = torch.max(outputs, 1)
+        return predicted.item()
+
+
+
+class TextGenerator:
+    def __init__(self, model_name='gpt2'):
+        self.generator = pipeline("text-generation", model=model_name)
+
+    def generate(self, prompt):
+        response = self.generator(prompt, max_length=100, num_return_sequences=1)
+        return response[0]['generated_text']
+
+
+
+
+class FAQRetriever:
+    def __init__(self, model_name='sentence-transformers/all-MiniLM-L6-v2'):
+        self.tokenizer = AutoTokenizer.from_pretrained(model_name)
+        self.model = AutoModel.from_pretrained(model_name)
+        self.index = faiss.IndexFlatL2(384)  # Dimension of MiniLM embeddings
+
+    def embed(self, text):
+        inputs = self.tokenizer(text, return_tensors='pt', truncation=True, padding=True)
+        with torch.no_grad():
+            embeddings = self.model(**inputs).last_hidden_state.mean(dim=1)
+        return embeddings.cpu().numpy()
+
+    def add_faqs(self, faqs):
+        self.faq_embeddings = np.concatenate([self.embed(faq) for faq in faqs])
+        faiss.normalize_L2(self.faq_embeddings)
+        self.index.add(self.faq_embeddings)
+
+    def retrieve(self, query):
+        query_embedding = self.embed(query)
+        faiss.normalize_L2(query_embedding)
+        D, I = self.index.search(query_embedding, 5)
+        return I[0]  # Return top 5 FAQ indices
+
+
+
+class CustomerSupportAssistant:
+    def __init__(self):
+        self.text_classifier = TextClassifier()
+        self.sentiment_analyzer = SentimentAnalyzer()
+        self.image_recognizer = ImageRecognizer()
+        self.text_generator = TextGenerator()
+        self.faq_retriever = FAQRetriever()
+        self.faqs = [
+            "How to reset my password?",
+            "What is the return policy?",
+            "How to track my order?",
+            "How to contact customer support?",
+            "What payment methods are accepted?"
+        ]
+        self.faq_retriever.add_faqs(self.faqs)
+
+    def process_query(self, text, image_path=None):
+        topic = self.text_classifier.classify(text)
+        sentiment = self.sentiment_analyzer.analyze(text)
+        if image_path:
+            image_info = self.image_recognizer.recognize(image_path)
+        else:
+            image_info = "No image provided."
+        faqs = self.faq_retriever.retrieve(text)
+        faq_responses = [self.faqs[i] for i in faqs]
+        response_prompt = f"Topic: {topic}, Sentiment: {sentiment['label']} with confidence {sentiment['score']}. FAQs: {faq_responses}. Image info: {image_info}. Generate a response."
+        response = self.text_generator.generate(response_prompt)
+        return response
+
+# Example usage:
+assistant = CustomerSupportAssistant()
+input_text = "I'm having trouble with my recent order."
+output = assistant.process_query(input_text)
+print(output)