Update app.py

app.py

@@ -1,98 +1,205 @@
-# Install necessary libraries
-# pip install transformers datasets torch
-
-from transformers import GPT2LMHeadModel, GPT2Tokenizer
-from transformers import Trainer, TrainingArguments
-from datasets import Dataset
-
-# Step 1: Load the pre-trained GPT-2 model and tokenizer
-model_name = "gpt2"  # You can use any GPT model, GPT-3, or other variants if you want a bigger model
-model = GPT2LMHeadModel.from_pretrained(model_name)
-tokenizer = GPT2Tokenizer.from_pretrained(model_name)
-
-# Set padding token as GPT-2 doesn't have one by default
-tokenizer.pad_token = tokenizer.eos_token
-
-# Step 2: Prepare your training data (Instagram algorithm and feature usage)
-training_data = [
-    {
-        "input": "How can I improve engagement on Instagram?",
-        "output": "Engagement can be improved by posting at optimal times, using 20-30 relevant hashtags, and responding to comments quickly. Consider using reels for higher visibility."
-    },
-    {
-        "input": "What are the best times to post on Instagram?",
-        "output": "The best times to post on Instagram depend on your audience's time zone. Typically, posting during peak activity times such as early morning or late evening can lead to better engagement."
-    },
-    {
-        "input": "How do I use Instagram Insights?",
-        "output": "Go to your profile, tap the menu, and select 'Insights.' You can view metrics like reach, impressions, and engagement."
-    },
-    {
-        "input": "What is the best way to use hashtags on Instagram?",
-        "output": "Use a mix of trending, niche, and brand-specific hashtags. Aim for around 20-30 relevant hashtags per post. Research the most effective ones for your target audience."
-    },
-    {
-        "input": "How can I use Instagram Stories to grow my account?",
-        "output": "Instagram Stories can be used to engage your followers by sharing behind-the-scenes content, polls, Q&As, and other interactive elements. Consistency and engaging content are key."
-    },
-]
-
-# Step 3: Process the data into a format suitable for training
-def process_data(examples):
-    # Concatenate input and output to form the training sequence
-    return tokenizer(examples['input'] + tokenizer.eos_token + examples['output'], truncation=True, padding="max_length", max_length=128)
-
-# Convert the training data into a dataset
-dataset = Dataset.from_dict(training_data)
-dataset = dataset.map(process_data, batched=True)
-
-# Step 4: Split the dataset into training and validation sets
-train_dataset = dataset.train_test_split(test_size=0.1)["train"]
-val_dataset = dataset.train_test_split(test_size=0.1)["test"]
-
-# Step 5: Define the training arguments
-training_args = TrainingArguments(
-    output_dir="./gpt2-instagram-model",     # Directory to save the model
-    evaluation_strategy="epoch",              # Evaluate at the end of each epoch
-    learning_rate=5e-5,                      # Learning rate for fine-tuning
-    per_device_train_batch_size=4,           # Batch size for training
-    per_device_eval_batch_size=4,            # Batch size for evaluation
-    num_train_epochs=3,                      # Number of training epochs
-    weight_decay=0.01,                       # Weight decay for regularization
-    logging_dir='./logs',                    # Log directory
-    logging_steps=200,                       # Log every 200 steps
+import re
+import json
+import numpy as np
+import faiss
+from flask import Flask, request, jsonify
+from transformers import (
+    pipeline,
+    AutoModelForSequenceClassification,
+    AutoTokenizer,
+    AutoModelForSeq2SeqLM,
+    AutoModelForCausalLM,
+    T5Tokenizer,
+    T5ForConditionalGeneration,
 )
+from sentence_transformers import SentenceTransformer
+from bertopic import BERTopic
+from datasets import load_dataset
+
+# Preprocessing function
+def preprocess_text(text):
+    """
+    Cleans and tokenizes text.
+    """
+    text = re.sub(r"http\S+|www\S+|https\S+", "", text, flags=re.MULTILINE)  # Remove URLs
+    text = re.sub(r"\s+", " ", text).strip()  # Remove extra spaces
+    text = re.sub(r"[^\w\s]", "", text)  # Remove punctuation
+    return text.lower()
+
+
+# Content Classification Model
+class ContentClassifier:
+    def __init__(self, model_name="bert-base-uncased"):
+        self.tokenizer = AutoTokenizer.from_pretrained(model_name)
+        self.model = AutoModelForSequenceClassification.from_pretrained(model_name)
+        self.pipeline = pipeline("text-classification", model=self.model, tokenizer=self.tokenizer)
+
+    def classify(self, text):
+        """
+        Classifies text into predefined categories.
+        """
+        result = self.pipeline(text)
+        return result
+
+
+# Relevance Detection Model
+class RelevanceDetector:
+    def __init__(self, model_name="bert-base-uncased"):
+        self.tokenizer = AutoTokenizer.from_pretrained(model_name)
+        self.model = AutoModelForSequenceClassification.from_pretrained(model_name)
+        self.pipeline = pipeline("text-classification", model=self.model, tokenizer=self.tokenizer)
+
+    def detect_relevance(self, text, threshold=0.5):
+        """
+        Detects whether a text is relevant to a specific domain.
+        """
+        result = self.pipeline(text)
+        return result[0]["label"] == "RELEVANT" and result[0]["score"] > threshold
+
+
+# Topic Extraction Model using BERTopic
+class TopicExtractor:
+    def __init__(self):
+        self.model = BERTopic()
+
+    def extract_topics(self, documents):
+        """
+        Extracts topics from a list of documents.
+        """
+        topics, probs = self.model.fit_transform(documents)
+        return self.model.get_topic_info()
+
+
+# Summarization Model
+class Summarizer:
+    def __init__(self, model_name="t5-small"):
+        self.tokenizer = T5Tokenizer.from_pretrained(model_name)
+        self.model = T5ForConditionalGeneration.from_pretrained(model_name)
+
+    def summarize(self, text, max_length=100):
+        """
+        Summarizes a given text.
+        """
+        inputs = self.tokenizer.encode("summarize: " + text, return_tensors="pt", max_length=512, truncation=True)
+        summary_ids = self.model.generate(inputs, max_length=max_length, min_length=25, length_penalty=2.0, num_beams=4)
+        summary = self.tokenizer.decode(summary_ids[0], skip_special_tokens=True)
+        return summary
+
+
+# Search and Recommendation Model using FAISS
+class SearchEngine:
+    def __init__(self, embedding_model="sentence-transformers/all-MiniLM-L6-v2"):
+        self.model = SentenceTransformer(embedding_model)
+        self.index = None
+        self.documents = []
+
+    def build_index(self, docs):
+        """
+        Builds a FAISS index for document retrieval.
+        """
+        self.documents = docs
+        embeddings = self.model.encode(docs, convert_to_tensor=True, show_progress_bar=True)
+        self.index = faiss.IndexFlatL2(embeddings.shape[1])
+        self.index.add(embeddings.cpu().detach().numpy())
+
+    def search(self, query, top_k=5):
+        """
+        Searches the index for the top_k most relevant documents.
+        """
+        query_embedding = self.model.encode(query, convert_to_tensor=True)
+        distances, indices = self.index.search(query_embedding.cpu().detach().numpy().reshape(1, -1), top_k)
+        return [(self.documents[i], distances[0][i]) for i in indices[0]]
+
+
+# Conversational Model using GPT-2
+class Chatbot:
+    def __init__(self, model_name="gpt2"):
+        self.tokenizer = AutoTokenizer.from_pretrained(model_name)
+        self.model = AutoModelForCausalLM.from_pretrained(model_name)
+
+    def generate_response(self, prompt, max_length=50):
+        """
+        Generates a response to a user query using GPT-2.
+        """
+        inputs = self.tokenizer.encode(prompt, return_tensors="pt")
+        outputs = self.model.generate(inputs, max_length=max_length, num_return_sequences=1)
+        response = self.tokenizer.decode(outputs[0], skip_special_tokens=True)
+        return response
+
+
+# Flask API for Chatbot Integration
+app = Flask(__name__)
+
+# Initialize models
+classifier = ContentClassifier()
+relevance_detector = RelevanceDetector()
+summarizer = Summarizer()
+search_engine = SearchEngine()
+topic_extractor = TopicExtractor()
+chatbot = Chatbot()
+
+# Load PleIAs/YouTube-Commons dataset
+def load_youtube_data():
+    dataset = load_dataset("PleIAs/YouTube-Commons")
+    return dataset["train"]["text"]  # Adjust based on dataset structure
+
+
+# Preprocess and build search index
+youtube_data = load_youtube_data()
+search_engine.build_index(youtube_data)
+
+# API Endpoints
+@app.route("/classify", methods=["POST"])
+def classify():
+    text = request.json.get("text", "")
+    if not text:
+        return jsonify({"error": "No text provided"}), 400
+    result = classifier.classify(text)
+    return jsonify(result)
+
+
+@app.route("/relevance", methods=["POST"])
+def relevance():
+    text = request.json.get("text", "")
+    if not text:
+        return jsonify({"error": "No text provided"}), 400
+    relevant = relevance_detector.detect_relevance(text)
+    return jsonify({"relevant": relevant})
+
+
+@app.route("/summarize", methods=["POST"])
+def summarize():
+    text = request.json.get("text", "")
+    if not text:
+        return jsonify({"error": "No text provided"}), 400
+    summary = summarizer.summarize(text)
+    return jsonify({"summary": summary})
+
+
+@app.route("/search", methods=["POST"])
+def search():
+    query = request.json.get("query", "")
+    if not query:
+        return jsonify({"error": "No query provided"}), 400
+    results = search_engine.search(query)
+    return jsonify({"results": results})
+
+
+@app.route("/topics", methods=["POST"])
+def topics():
+    result = topic_extractor.extract_topics(youtube_data)
+    return jsonify({"topics": result.to_dict()})
+
+
+@app.route("/chat", methods=["POST"])
+def chat():
+    prompt = request.json.get("prompt", "")
+    if not prompt:
+        return jsonify({"error": "No prompt provided"}), 400
+    response = chatbot.generate_response(prompt)
+    return jsonify({"response": response})
 
-# Step 6: Initialize the Trainer
-trainer = Trainer(
-    model=model,                             # The model we are training
-    args=training_args,                      # Training arguments
-    train_dataset=train_dataset,             # Training dataset
-    eval_dataset=val_dataset,                # Validation dataset
-)
 
-# Step 7: Train the model
-trainer.train()
-
-# Step 8: Evaluate the model after training
-results = trainer.evaluate()
-print("Evaluation Results:", results)
-
-# Step 9: Save the model and tokenizer
-model.save_pretrained("./gpt2-instagram-model")
-tokenizer.save_pretrained("./gpt2-instagram-model")
-
-# Step 10: Use the trained model to generate responses
-def generate_response(input_text):
-    # Encode the input text and generate a response
-    inputs = tokenizer.encode(input_text, return_tensors="pt")
-    output = model.generate(inputs, max_length=100, num_return_sequences=1, no_repeat_ngram_size=2)
-    
-    # Decode and return the response
-    response = tokenizer.decode(output[0], skip_special_tokens=True)
-    return response
-
-# Example: Generate a response
-input_text = "How can I improve engagement on Instagram?"
-response = generate_response(input_text)
-print("Generated Response:", response)
+# Start the Flask API
+if __name__ == "__main__":
+    app.run(debug=True)