app.py

app.py

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+#Import Libraries
+import torch
+from transformers import BlipProcessor, BlipForConditionalGeneration, AutoTokenizer, AutoModelForSeq2SeqLM
+from PIL import Image
+import gradio as gr
+import difflib
+#Load Models
+device = "cuda" if torch.cuda.is_available() else "cpu"
+#BLIP for image captioning
+blip_processor = BlipProcessor.from_pretrained("Salesforce/blip-image-captioning-base")
+blip_model = BlipForConditionalGeneration.from_pretrained("Salesforce/blip-image-captioning-base").to(device)
+#FLAN-T5 for language generation
+tokenizer = AutoTokenizer.from_pretrained("google/flan-t5-large")
+model = AutoModelForSeq2SeqLM.from_pretrained("google/flan-t5-large").to(device)
+#Image Captioning
+def describe_image(img):
+    try:
+        inputs = blip_processor(images=img, return_tensors="pt").to(device)
+        out = blip_model.generate(
+            **inputs,
+            max_length=50,
+            num_beams=5,
+            repetition_penalty=1.8,
+            no_repeat_ngram_size=3,
+            temperature=0.7
+        )
+        caption = blip_processor.decode(out[0], skip_special_tokens=True)
+        # Post-process: Remove duplicate words
+        words = caption.split()
+        seen = set()
+        deduped = []
+        for word in words:
+            if word not in seen:
+                deduped.append(word)
+                seen.add(word)
+        return ' '.join(deduped).capitalize()
+    except Exception as e:
+        return f"Error analyzing image: {e}"
+#Classify Text Queries (if needed)
+FAQ_KEYWORDS = ["landlord", "tenant", "rent", "deposit", "notice", "agreement", "eviction", "lease", "contract"]
+def classify_query(text):
+    words = text.lower().split()
+    matches = [difflib.get_close_matches(word, FAQ_KEYWORDS, cutoff=0.7) for word in words]
+    if any(matches):
+        return "FAQ"
+    return "Issue"
+#Agent 1 – Property Issue Detector
+def agent1(image, text):
+    caption = describe_image(image)
+    combined = f"Image shows: {caption}."
+    if text:
+        combined += f" User also says: {text}"
+
+    prompt = f"""
+You are a property repair expert. Based on this input, identify any issues with the property and suggest relevant troubleshooting actions.
+
+{combined}
+    """
+    inputs = tokenizer(prompt, return_tensors="pt").to(device)
+    outputs = model.generate(**inputs, max_length=256)
+    response = tokenizer.decode(outputs[0], skip_special_tokens=True)
+
+    return f"**Image Description:** {caption}\n\n**Suggested Fix:** {response}"
+#Agent 2 – Tenancy FAQ Handler
+def agent2(text, location=None):
+    if not text:
+        return "Please ask a question related to tenancy."   
+    loc_text = f"This question is from a user in {location}.\n" if location else ""
+    prompt = f"""
+You are a legal assistant who answers tenancy-related questions. {loc_text}
+Provide a helpful, friendly, and legally accurate response to the following question:
+
+{text}
+    """
+    inputs = tokenizer(prompt, return_tensors="pt").to(device)
+    outputs = model.generate(**inputs, max_length=256)
+    return tokenizer.decode(outputs[0], skip_special_tokens=True)
+#Gradio Tabs UI
+#Tab 1: Property Issue Detection
+agent1_tab = gr.Interface(
+    fn=agent1,
+    inputs=[
+        gr.Image(type="pil", label="Upload Property Image"),
+        gr.Textbox(lines=3, label="Description (optional)")
+    ],
+    outputs=gr.Markdown(label="Analysis & Troubleshooting"),
+    title="🛠 Property Issue Detector",
+    description="Upload a property image and optionally describe the issue. The bot will analyze and give troubleshooting suggestions."
+)
+# Tab 2: Tenancy FAQ
+agent2_tab = gr.Interface(
+    fn=agent2,
+    inputs=[
+        gr.Textbox(lines=3, label="Your Tenancy-related Question"),
+        gr.Textbox(lines=1, label="City/Region (optional)")
+    ],
+    outputs=gr.Markdown(label="Legal Advice"),
+    title="Tenancy FAQ Assistant",
+    description="Ask tenancy-related questions like eviction, rent, deposits, etc. Add your city or region for more accurate responses."
+)
+#Launch the full app with tabs
+gr.TabbedInterface(
+    [agent1_tab, agent2_tab],
+    tab_names=["Property Issue Detection", "Tenancy FAQ Assistant"]
+).launch(share=True)