Upload inference_api.py

inference_api.py

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+# inference_api.py
+import os
+import fitz  # PyMuPDF
+import fasttext
+import torch
+from PIL import Image
+from huggingface_hub import hf_hub_download
+from transformers import AutoTokenizer, AutoModelForSeq2SeqLM, pipeline
+from IndicTransToolkit.processor import IndicProcessor
+import google.generativeai as genai
+from fastapi import FastAPI
+from pydantic import BaseModel
+from typing import Optional
+import json
+
+app = FastAPI()
+
+# === CONFIGURATION ===
+GEMINI_API_KEY = os.environ.get("GEMINI_API_KEY")
+TRANSLATION_MODEL_REPO_ID = "ai4bharat/indictrans2-indic-en-1B"
+OCR_MODEL_ID = "microsoft/trocr-base-printed"
+LANGUAGE_TO_TRANSLATE = "mal"
+DEVICE = "cpu"
+
+# --- Configure Gemini ---
+if GEMINI_API_KEY:
+    genai.configure(api_key=GEMINI_API_KEY)
+else:
+    print("🔴 GEMINI_API_KEY not set.")
+
+# --- Load Models ---
+translation_tokenizer = AutoTokenizer.from_pretrained(TRANSLATION_MODEL_REPO_ID, trust_remote_code=True)
+translation_model = AutoModelForSeq2SeqLM.from_pretrained(
+    TRANSLATION_MODEL_REPO_ID, trust_remote_code=True, torch_dtype=torch.float32
+).to(DEVICE)
+ip = IndicProcessor(inference=True)
+
+ft_model_path = hf_hub_download(repo_id="facebook/fasttext-language-identification", filename="model.bin")
+lang_detect_model = fasttext.load_model(ft_model_path)
+
+ocr_pipeline = pipeline("image-to-text", model=OCR_MODEL_ID, device=-1)
+
+# === HELPER FUNCTIONS ===
+def classify_image_with_gemini(image: Image.Image):
+    model = genai.GenerativeModel('gemini-1.5-flash-latest')
+    prompt = "Is this image primarily a text document or an engineering/technical diagram? Answer with only 'document' or 'diagram'."
+    response = model.generate_content([prompt, image])
+    classification = response.text.strip().lower()
+    return "diagram" if "diagram" in classification else "document"
+
+def summarize_diagram_with_gemini(image: Image.Image):
+    model = genai.GenerativeModel('gemini-1.5-flash-latest')
+    prompt = "Describe the contents of this technical diagram in a concise summary."
+    response = model.generate_content([prompt, image])
+    return response.text.strip()
+
+def extract_text_from_image(path):
+    image = Image.open(path).convert("RGB")
+    image_type = classify_image_with_gemini(image)
+    if image_type == "diagram":
+        return summarize_diagram_with_gemini(image)
+    else:
+        out = ocr_pipeline(image)
+        return out[0]["generated_text"] if out else ""
+
+def extract_text_from_pdf(path):
+    doc = fitz.open(path)
+    return "".join(page.get_text("text") + "\n" for page in doc)
+
+def read_text_from_txt(path):
+    with open(path, "r", encoding="utf-8") as f:
+        return f.read()
+
+def detect_language(text_snippet):
+    s = text_snippet.replace("\n", " ").strip()
+    if not s: return None
+    preds = lang_detect_model.predict(s, k=1)
+    return preds[0][0].split("__")[-1] if preds and preds[0] else None
+
+def translate_chunk(chunk):
+    batch = ip.preprocess_batch([chunk], src_lang="mal_Mlym", tgt_lang="eng_Latn")
+    inputs = translation_tokenizer(batch, return_tensors="pt", padding=True, truncation=True, max_length=512).to(DEVICE)
+    with torch.no_grad():
+        generated_tokens = translation_model.generate(**inputs, num_beams=5, max_length=512, early_stopping=True)
+    decoded = translation_tokenizer.batch_decode(generated_tokens, skip_special_tokens=True)
+    return ip.postprocess_batch(decoded, lang="eng_Latn")[0]
+
+def generate_structured_json(text_to_analyze):
+    model = genai.GenerativeModel('gemini-1.5-flash-latest')
+    prompt = f"Analyze this document and extract key info as JSON: {text_to_analyze}"
+    json_schema = {
+        "type": "OBJECT",
+        "properties": {
+            "summary": {"type": "STRING"},
+            "actions_required": {"type": "ARRAY", "items": {
+                "type": "OBJECT",
+                "properties": {"action": {"type": "STRING"}, "priority": {"type": "STRING", "enum": ["High","Medium","Low"]}, "deadline": {"type": "STRING"}, "notes": {"type": "STRING"}},
+                "required": ["action","priority","deadline","notes"]
+            }},
+            "departments_to_notify": {"type": "ARRAY", "items": {"type": "STRING"}},
+            "cross_document_flags": {"type": "ARRAY", "items": {
+                "type": "OBJECT",
+                "properties": {"related_document_type": {"type": "STRING"}, "related_issue": {"type": "STRING"}},
+                "required": ["related_document_type","related_issue"]
+            }}
+        },
+        "required": ["summary","actions_required","departments_to_notify","cross_document_flags"]
+    }
+    generation_config = genai.types.GenerationConfig(response_mime_type="application/json", response_schema=json_schema)
+    response = model.generate_content(prompt, generation_config=generation_config)
+    return json.loads(response.text)
+
+def check_relevance_with_gemini(summary_text):
+    model = genai.GenerativeModel('gemini-1.5-flash-latest')
+    prompt = f'Is this summary relevant to transportation, infrastructure, railways, or metro systems? Answer "Yes" or "No". Summary: {summary_text}'
+    response = model.generate_content(prompt)
+    return "yes" in response.text.strip().lower()
+
+# === API INPUT SCHEMA ===
+class InputFile(BaseModel):
+    file_path: str
+
+@app.post("/predict")
+def predict(file: InputFile):
+    if not GEMINI_API_KEY:
+        return {"error": "Gemini API key not set."}
+    path = file.file_path
+    ext = os.path.splitext(path)[1].lower()
+    
+    # Phase 1: Extract text
+    if ext == ".pdf":
+        original_text = extract_text_from_pdf(path)
+    elif ext == ".txt":
+        original_text = read_text_from_txt(path)
+    elif ext in [".png", ".jpg", ".jpeg"]:
+        original_text = extract_text_from_image(path)
+    else:
+        return {"error": "Unsupported file type."}
+    
+    # Phase 2: Translate Malayalam if detected
+    lines = original_text.split("\n")
+    translated_lines = []
+    for ln in lines:
+        if not ln.strip(): continue
+        lang = detect_language(ln)
+        if lang == LANGUAGE_TO_TRANSLATE:
+            translated_lines.append(translate_chunk(ln))
+        else:
+            translated_lines.append(ln)
+    final_text = "\n".join(translated_lines)
+    
+    # Phase 3: Gemini analysis
+    summary_data = generate_structured_json(final_text)
+    if not summary_data or "summary" not in summary_data:
+        return {"error": "Failed to generate analysis."}
+    
+    is_relevant = check_relevance_with_gemini(summary_data["summary"])
+    if is_relevant:
+        return summary_data
+    else:
+        return {"status": "Not Applicable", "reason": "Document not relevant to KMRL."}