Update app.py

app.py

@@ -2,14 +2,14 @@ import os
 import pandas as pd
 import numpy as np
 import re
-from llm import call_llm  
+import gradio as gr
 from openai import OpenAI
 import base64
-from fastapi import FastAPI, UploadFile, File, Form
-from typing import List, Optional
-import uvicorn
-
 
+client = OpenAI(
+    base_url="https://openrouter.ai/api/v1",
+    api_key=os.getenv("OPENROUTER_API_KEY")  
+)
 
 # ---------------- IMAGE MODERATION FUNCTION ---------------- #
 def check_image_safety(image_paths):
@@ -29,7 +29,15 @@ def check_image_safety(image_paths):
             with open(image_path, "rb") as img_file:
                 img_base64 = base64.b64encode(img_file.read()).decode("utf-8")
 
-            prompt =  """Analyze this image carefully for any unsafe or inappropriate content.
+            response = client.chat.completions.create(
+                model="openai/gpt-5-mini",
+                messages=[
+                    {
+                        "role": "user",
+                        "content": [
+                            {
+                                "type": "text",
+                                "text": """Analyze this image carefully for any unsafe or inappropriate content.
 
 You must respond with EXACTLY ONE WORD: 'safe' or 'unsafe'. Do NOT explain.
 
@@ -46,9 +54,21 @@ Remember:
 
 Return only: safe OR unsafe.
 """
-            verdict = call_llm(prompt, images=[img_base64], max_tokens=200, temperature=0.5),
-            verdict_lower = verdict.lower()
-            if "unsafe" in verdict_lower:
+
+                            },
+                            {
+                                "type": "image_url",
+                                "image_url": f"data:image/jpeg;base64,{img_base64}"
+                            }
+                        ]
+                    }
+                ],
+                temperature=0.5,
+                max_tokens=200,
+            )
+
+            verdict = response.choices[0].message.content.strip().lower()
+            if "unsafe" in verdict:
                 return "unsafe"
 
         return "safe"
@@ -97,8 +117,14 @@ Review: {review_text}
 Sentiment:
 """
     try:
-        raw = call_llm(prompt, max_tokens=500, temperature=0.2)  # max_tokens 50–100 enough for sentiment
-    
+        response = client.chat.completions.create(
+            model="openai/gpt-5-mini",
+            messages=[{"role": "user", "content": prompt}],
+            temperature=0.2,
+            max_tokens=500
+        )
+
+        raw = response.choices[0].message.content or ""
         raw_lower = raw.lower().strip()
 
         if "positive" in raw_lower:
@@ -221,40 +247,24 @@ def validate_review(description, rating, image_paths=None):
     return data['Labeled Result'][0], LLM, image_flag
 
 
-app = FastAPI(
-    title="Priceoye Review Moderation API",
-    version="1.0"
-)
+# ---------------- GRADIO INTERFACE ---------------- #
+def classify_review(description, rating, images):
+    image_paths = [img.name for img in images] if images else None
+    result, sentiment, image_flag = validate_review(description, rating, image_paths)
+    return f"Sentiment: {sentiment}\nImage Safety: {image_flag}\nDecision: {result}"
 
-@app.post("/classify-review")
-async def classify_review(
-    description: str = Form(""),
-    rating: str = Form(...),
-    images: Optional[List[UploadFile]] = File(None)
-):
-    image_paths = []
-
-    if images:
-        for img in images[:3]:  # max 3 images
-            file_path = f"/tmp/{img.filename}"
-            with open(file_path, "wb") as f:
-                f.write(await img.read())
-            image_paths.append(file_path)
-
-    decision, sentiment, image_flag = validate_review(
-        description, rating, image_paths
-    )
 
-    return {
-        "sentiment": sentiment,
-        "image_safety": image_flag,
-        "decision": decision
-    }
-if __name__ == "__main__":
-    uvicorn.run(
-        "app:app",
-        host="0.0.0.0",
-        port=7860,
-        log_level="info"
-    )
+iface = gr.Interface(
+    fn=classify_review,
+    inputs=[
+        gr.Textbox(label="Review Description"),
+        gr.Radio(["1", "2", "3", "4", "5"], label="Rating"),
+        gr.Files(label="Upload Images (optional)")
+    ],
+    outputs="text",
+    title="Priceoye Review Classifier (with Image Moderation)",
+    description="Classifies reviews as accepted/rejected/ignored using LLM + rule logic + image safety check."
+)
 
+if __name__ == "__main__":
+    iface.launch()