Create app.py

app.py

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+import gradio as gr
+import fitz  # PyMuPDF for PDF text extraction
+import json
+import torch
+import transformers
+from transformers import AutoTokenizer, AutoModelForCausalLM, pipeline
+import re
+
+# Constants
+MODEL_NAME = "Qwen/Qwen2.5-1.5B-Instruct"
+DEVICE = "cpu"  # Change to "cuda" if GPU is enabled in Space
+
+# Load model once
+tokenizer = AutoTokenizer.from_pretrained(MODEL_NAME, trust_remote_code=True)
+model_config = transformers.AutoConfig.from_pretrained(MODEL_NAME, trust_remote_code=True)
+model = AutoModelForCausalLM.from_pretrained(
+    MODEL_NAME,
+    config=model_config,
+    device_map="auto",
+    torch_dtype=torch.float32,
+    trust_remote_code=True
+)
+generator = pipeline(
+    "text-generation",
+    model=model,
+    tokenizer=tokenizer,
+    eos_token_id=tokenizer.eos_token_id,
+    pad_token_id=tokenizer.eos_token_id,
+    max_new_tokens=1000,
+)
+
+def extract_text_from_pdf(pdf_path):
+    doc = fitz.open(pdf_path)
+    text = "\n".join(page.get_text("text") for page in doc)
+    return text if text.strip() else "Error: No extractable text found in PDF."
+
+def build_prompt(text):
+    instruction = f"""
+You are an AI that extracts structured metadata from research papers.
+Extract the following fields and return ONLY valid JSON (no extra text, no markdown, no explanations):
+{{
+  "Title": "Paper title",
+  "Authors": ["Author 1", "Author 2"],
+  "DOI": "DOI if available",
+  "Keywords": ["Keyword1", "Keyword2"],
+  "Abstract": "Abstract text"
+}}
+Here is the paper content:
+{text[:3000]}
+"""
+    return (
+        "<|im_start|>system\n"
+        "You are a helpful assistant that extracts structured metadata from scientific papers.\n"
+        "<|im_end|>\n"
+        "<|im_start|>user\n"
+        f"{instruction.strip()}\n"
+        "<|im_end|>\n"
+        "<|im_start|>assistant"
+    )
+
+def extract_json(text):
+    assistant_start = text.find("<|im_start|>assistant")
+    if assistant_start == -1:
+        return {"Error": "No assistant section found in output"}
+
+    assistant_text = text[assistant_start:]
+    assistant_text = re.sub(r"```(?:json)?|```", "", assistant_text).strip()
+
+    start = assistant_text.find('{')
+    if start == -1:
+        return {"Error": "No opening '{' found in assistant section"}
+
+    brace_count = 0
+    for i in range(start, len(assistant_text)):
+        if assistant_text[i] == '{':
+            brace_count += 1
+        elif assistant_text[i] == '}':
+            brace_count -= 1
+            if brace_count == 0:
+                json_str = assistant_text[start:i+1]
+                try:
+                    return json.loads(json_str)
+                except Exception as e:
+                    return {"Error": f"JSON parse failed: {e}"}
+
+    return {"Error": "No complete JSON block found"}
+
+def extract_metadata(paper_text):
+    prompt = build_prompt(paper_text)
+    response = generator(prompt, max_new_tokens=1000, do_sample=False, temperature=0)
+    raw_output = response[0]["generated_text"]
+    return extract_json(raw_output)
+
+def process_pdf(pdf_file):
+    extracted_text = extract_text_from_pdf(pdf_file.name)
+    if extracted_text.startswith("Error:"):
+        return {"Error": "No extractable text found in the PDF."}
+    metadata = extract_metadata(extracted_text)
+    return metadata
+
+# Gradio interface
+iface = gr.Interface(
+    fn=process_pdf,
+    inputs=gr.File(label="Upload PDF"),
+    outputs="json",
+    title="Metadata Extractor",
+    description="Upload a research PDF to extract structured metadata fields."
+)
+
+iface.launch()