Using BERT and GPT-4

Dockerfile

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+# Use Python 3.9
+FROM python:3.9
+
+# Create a non-root user
+RUN useradd -m -u 1000 user
+USER user
+ENV PATH="/home/user/.local/bin:$PATH"
+
+# Set working directory
+WORKDIR /app
+
+# Copy requirements and install dependencies
+COPY --chown=user ./requirements.txt requirements.txt
+RUN pip install --no-cache-dir --upgrade -r requirements.txt
+
+# Copy the rest of the application
+COPY --chown=user . /app
+
+# Run the FastAPI app with Uvicorn
+CMD ["uvicorn", "app:app", "--host", "0.0.0.0", "--port", "7860"]

app.py

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+import os
+from openai import OpenAI  # Use only this import
+from fastapi import FastAPI, HTTPException
+from pydantic import BaseModel
+from transformers import pipeline
+from keybert import KeyBERT  # For BERT-based keyword extraction
+
+app = FastAPI()
+
+# Load BERT model for key point extraction
+kw_model = KeyBERT()
+
+# Get OpenAI API key from environment variable
+OPENAI_API_KEY = os.getenv("OPENAI_API_KEY")
+if not OPENAI_API_KEY:
+    raise ValueError("OpenAI API key is missing. Set OPENAI_API_KEY as an environment variable.")
+
+# Initialize OpenAI client
+client = OpenAI(api_key=OPENAI_API_KEY)
+
+# Define request format
+class SummarizationRequest(BaseModel):
+    text: str
+
+@app.post("/summarize")
+def summarize_text(request: SummarizationRequest):
+    if not request.text.strip():
+        raise HTTPException(status_code=400, detail="No text provided")
+
+    # Step 1: Extract key points using BERT (KeyBERT)
+    key_points = kw_model.extract_keywords(request.text, keyphrase_ngram_range=(1, 2), stop_words='english', top_n=5)
+    extracted_points = ", ".join([kp[0] for kp in key_points])
+
+    # Step 2: Generate summary using GPT-4
+    try:
+        response = client.chat.completions.create(
+            model="gpt-4",
+            messages=[
+                {"role": "system", "content": "You are an AI assistant that summarizes text."},
+                {"role": "user", "content": f"Summarize the following text in a concise way:\n\n{request.text}"}
+            ]
+        )
+        summary = response.choices[0].message.content  # Correct response parsing
+
+    except Exception as e:
+        raise HTTPException(status_code=500, detail=f"Error with GPT-4 API: {str(e)}")
+
+    return {
+        "key_points": extracted_points,
+        "summary": summary
+    }
+
+@app.get("/")
+def greet_json():
+    return {"message": "Welcome to the AI Summarizer API!"}

requirements.txt

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+fastapi
+uvicorn[standard]
+openai>=1.0.0
+transformers
+keybert
+sentence-transformers