Create pipeline.py

pipeline.py

@@ -0,0 +1,41 @@
+import pytesseract
+from pdf2image import convert_from_path
+import google.generativeai as genai
+import os, json
+
+# --- Configure Gemini ---
+api_key = os.getenv("GEMINI_API_KEY")
+if not api_key:
+    raise ValueError("❌ GEMINI_API_KEY not found. Please set it in Hugging Face Space Secrets.")
+genai.configure(api_key=api_key)
+
+def extract_text_from_pdf(pdf_path):
+    pages = convert_from_path(pdf_path)
+    text = ""
+    for page in pages:
+        text += pytesseract.image_to_string(page) + "\n"
+    return text.strip()
+
+def extract_key_values_with_gemini(raw_text, fields):
+    prompt = f"""
+You are an intelligent document parser.
+Given the following document text, extract only these fields: {fields}.
+Return strictly as JSON key-value pairs.
+Document text:
+{raw_text}
+"""
+    model = genai.GenerativeModel("models/gemini-2.5-flash")
+    response = model.generate_content(prompt)
+    text = response.text.strip()
+
+    # --- Cleanup ---
+    text = text.replace("```json", "").replace("```", "").strip()
+
+    try:
+        extracted = json.loads(text)
+    except Exception:
+        extracted = {"raw_output": text}
+
+    # --- Ensure all fields exist ---
+    result = {field: extracted.get(field, "") for field in fields}
+    return result