Update app.py

app.py

@@ -1,30 +1,15 @@
-import torch
-from transformers import AutoTokenizer, AutoModelForCausalLM
 import gradio as gr
+from transformers import pipeline
 from PyPDF2 import PdfReader
 
 # -----------------------------
-# Load model with fallback
+# Load HuggingFace pipelines
 # -----------------------------
-MODEL_NAME = "distilgpt2"  # fallback if granite not available
-
-try:
-    tokenizer = AutoTokenizer.from_pretrained(MODEL_NAME, trust_remote_code=True)
-    model = AutoModelForCausalLM.from_pretrained(
-        MODEL_NAME,
-        trust_remote_code=True,
-        torch_dtype=torch.float32,
-        device_map="auto"
-    )
-except Exception as e:
-    print(f"⚠️ Model load failed: {e}")
-    print("Falling back to distilgpt2...")
-    tokenizer = AutoTokenizer.from_pretrained("distilgpt2")
-    model = AutoModelForCausalLM.from_pretrained("distilgpt2")
-
-# Ensure pad token exists
-if tokenizer.pad_token is None:
-    tokenizer.pad_token = tokenizer.eos_token
+# Small, lightweight generation model
+generator = pipeline("text-generation", model="distilgpt2")
+
+# Summarization model
+summarizer = pipeline("summarization", model="facebook/bart-large-cnn")
 
 
 # -----------------------------
@@ -33,67 +18,96 @@ if tokenizer.pad_token is None:
 def extract_text_from_pdf(pdf_file):
     text = ""
     try:
-        # pdf_file is a dict in Gradio (with 'name' key)
         file_path = pdf_file.name if hasattr(pdf_file, "name") else pdf_file
         reader = PdfReader(file_path)
         for page in reader.pages:
-            text += page.extract_text() or ""
+            page_text = page.extract_text()
+            if page_text:
+                text += page_text + "\n"
     except Exception as e:
         text = f"❌ Error reading PDF: {e}"
-    return text
+    return text.strip()
 
 
 # -----------------------------
-# Response generation
+# Eco Tips Generator
 # -----------------------------
-def generate_response(pdf_file, user_query):
-    pdf_text = extract_text_from_pdf(pdf_file)
-    if not pdf_text.strip():
-        return "⚠️ Could not extract text from the PDF."
-
-    # Build prompt
-    prompt = f"""
-You are a Sustainable Smart City Assistant. 
-Here is context from a PDF document:
+def eco_tips_generator(problem_keywords):
+    if not problem_keywords.strip():
+        return "⚠️ Please enter some keywords."
 
-{pdf_text[:2000]}  # limit context length to avoid overflow
+    prompt = f"Give 5 practical eco-friendly tips related to {problem_keywords}:"
+    result = generator(prompt, max_new_tokens=120, num_return_sequences=1, temperature=0.7)
+    return result[0]["generated_text"]
 
-User query: {user_query}
 
-Answer clearly and concisely:
-"""
+# -----------------------------
+# Policy Summarization
+# -----------------------------
+def policy_summarization(pdf_file, policy_text):
+    # Extract text from PDF or use textbox input
+    content = ""
+    if pdf_file is not None:
+        content = extract_text_from_pdf(pdf_file)
+    elif policy_text.strip():
+        content = policy_text.strip()
 
-    inputs = tokenizer(prompt, return_tensors="pt", truncation=True).to(model.device)
+    if not content:
+        return "⚠️ Please upload a PDF or paste some policy text."
 
-    with torch.no_grad():
-        outputs = model.generate(
-            **inputs,
-            max_new_tokens=200,
-            temperature=0.7,
-            pad_token_id=tokenizer.eos_token_id
-        )
+    # Limit input size for summarizer
+    content = content[:3000]
 
-    response = tokenizer.decode(outputs[0], skip_special_tokens=True)
-    return response
+    try:
+        summary = summarizer(content, max_length=200, min_length=60, do_sample=False)
+        return summary[0]["summary_text"]
+    except Exception as e:
+        return f"❌ Error during summarization: {e}"
 
 
 # -----------------------------
-# Gradio UI
+# Gradio Interface
 # -----------------------------
 with gr.Blocks() as app:
-    gr.Markdown("## 🌍 Sustainable Smart City Assistant")
-
-    with gr.Row():
-        pdf_file = gr.File(label="Upload PDF", type="file")
-        user_query = gr.Textbox(label="Ask your question")
-
-    output = gr.Textbox(label="Assistant Response")
-
-    submit = gr.Button("Get Answer")
-    submit.click(fn=generate_response, inputs=[pdf_file, user_query], outputs=output)
+    gr.Markdown("# 🌍 Eco Assistant & Policy Analyzer")
+
+    with gr.Tabs():
+        # Eco Tips Tab
+        with gr.TabItem("♻️ Eco Tips Generator"):
+            with gr.Row():
+                with gr.Column():
+                    keywords_input = gr.Textbox(
+                        label="Environmental Problem/Keywords",
+                        placeholder="e.g., plastic, solar, water waste, energy saving...",
+                        lines=2
+                    )
+                    generate_tips_btn = gr.Button("Generate Eco Tips")
+
+                with gr.Column():
+                    tips_output = gr.Textbox(label="Sustainable Living Tips", lines=10)
+
+            generate_tips_btn.click(eco_tips_generator, inputs=keywords_input, outputs=tips_output)
+
+        # Policy Summarization Tab
+        with gr.TabItem("📑 Policy Summarization"):
+            with gr.Row():
+                with gr.Column():
+                    pdf_upload = gr.File(label="Upload Policy PDF", file_types=[".pdf"])
+                    policy_text_input = gr.Textbox(
+                        label="Or paste policy text here",
+                        placeholder="Paste policy document text...",
+                        lines=5
+                    )
+                    summarize_btn = gr.Button("Summarize Policy")
+
+                with gr.Column():
+                    summary_output = gr.Textbox(label="Policy Summary & Key Points", lines=15)
+
+            summarize_btn.click(policy_summarization, inputs=[pdf_upload, policy_text_input], outputs=summary_output)
 
 # -----------------------------
 # Run app
 # -----------------------------
 if __name__ == "__main__":
     app.launch()
+