Add application file

Dockerfile

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+FROM python:3.11-slim
+
+WORKDIR /app
+
+COPY requirements.txt .
+RUN pip install --no-cache-dir -r requirements.txt
+
+COPY . .
+
+EXPOSE 7860
+
+CMD ["uvicorn", "app:app", "--host", "0.0.0.0", "--port", "7860"]

app.py

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+from fastapi import FastAPI, File, UploadFile, HTTPException
+from fastapi.middleware.cors import CORSMiddleware
+from fastapi.staticfiles import StaticFiles
+from fastapi.responses import FileResponse
+from pydantic import BaseModel
+import requests
+import base64
+import json
+import re
+import os
+
+app = FastAPI(
+    title="Aadhaar Card OCR API",
+    description="Two-step RAG pipeline: nemoretriever-ocr-v1 → nvidia-nemotron-nano-9b-v2 for Aadhaar card extraction",
+)
+
+# ── CORS ──────────────────────────────────────────────────────────────────────
+app.add_middleware(
+    CORSMiddleware,
+    allow_origins=["*"],
+    allow_credentials=True,
+    allow_methods=["*"],
+    allow_headers=["*"],
+)
+
+# ── Configuration ─────────────────────────────────────────────────────────────
+# Set NVIDIA_API_KEY as a Secret in your HuggingFace Space settings
+NVIDIA_API_KEY = os.environ.get("NVIDIA_API_KEY", "")
+
+OCR_URL = "https://ai.api.nvidia.com/v1/cv/nvidia/nemoretriever-ocr-v1"
+LLM_URL = "https://integrate.api.nvidia.com/v1/chat/completions"
+LLM_MODEL = "nvidia/nvidia-nemotron-nano-9b-v2"
+
+OCR_HEADERS = {
+    "Authorization": f"Bearer {NVIDIA_API_KEY}",
+    "Accept": "application/json",
+}
+
+LLM_HEADERS = {
+    "Authorization": f"Bearer {NVIDIA_API_KEY}",
+    "Content-Type": "application/json",
+}
+
+# ── System prompts ─────────────────────────────────────────────────────────────
+
+FRONT_SYSTEM_PROMPT = """You are an Aadhaar card front-side data extraction assistant.
+You will receive raw OCR text extracted from the FRONT side of an Indian Aadhaar card.
+Parse it carefully and return ONLY a valid JSON object.
+No markdown fences, no explanation, no preamble — just the raw JSON object.
+
+JSON schema (return exactly this structure):
+{
+  "name": "full name of the card holder (string)",
+  "dob": "date of birth in DD/MM/YYYY format (string)",
+  "gender": "gender Male, Female, or Other (string)",
+  "aadhaar_no": "12-digit Aadhaar number as a string, digits only, yes spaces"
+}
+
+Rules:
+- name: the primary card holder's full name (usually in bold, after "Name:" or just prominently placed)
+- dob: look for "DOB", "Date of Birth", "जन्म तिथि" — output in DD/MM/YYYY format; if already in that format keep it
+- gender: look for "Male", "Female", "Other", or Hindi equivalents "पुरुष", "महिला", "अन्य"
+- aadhaar_no: the 12-digit number, usually printed in groups like "XXXX XXXX XXXX" — remove all spaces and return only digits
+- If a field is not found, use "" for strings
+- Do NOT include address details, gender, or any other fields not in the schema"""
+
+BACK_SYSTEM_PROMPT = """You are an Aadhaar card back-side data extraction assistant.
+You will receive raw OCR text extracted from the BACK side of an Indian Aadhaar card.
+Parse it carefully and return ONLY a valid JSON object.
+No markdown fences, no explanation, no preamble — just the raw JSON object.
+
+JSON schema (return exactly this structure):
+{
+  "address": "door/flat number and street/locality/road name (string)",
+  "village_city": "village name or city/town name (string)",
+  "state": "state name (string)",
+  "pincode": "6-digit PIN code as a string"
+}
+
+Rules:
+- address: the first line(s) of the address — house/flat number, building name, street or locality; exclude city, district, state, and PIN
+- village_city: look for village name, town, or city; may also appear under "District" — prefer the more specific locality name over the district
+- state: the full state name (e.g. "Maharashtra", "Tamil Nadu"); look near the end of the address block
+- pincode: the 6-digit postal code; look for "PIN", "PIN Code", or a standalone 6-digit number at the end of the address
+- If a field is not found, use "" for strings
+- Do NOT include the card holder's name or Aadhaar number — focus only on address fields"""
+
+
+# ── Helper ─────────────────────────────────────────────────────────────────────
+
+async def run_ocr(file: UploadFile) -> str:
+    """Upload image to NVIDIA OCR and return concatenated plain text."""
+    content = await file.read()
+    image_b64 = base64.b64encode(content).decode()
+
+    if len(image_b64) >= 180_000:
+        raise HTTPException(
+            status_code=413,
+            detail="Image too large (base64 > 180 KB). Resize the image and try again.",
+        )
+
+    payload = {
+        "input": [
+            {
+                "type": "image_url",
+                "url": f"data:image/png;base64,{image_b64}",
+            }
+        ]
+    }
+
+    try:
+        response = requests.post(OCR_URL, headers=OCR_HEADERS, json=payload, timeout=30)
+        response.raise_for_status()
+    except requests.exceptions.RequestException as e:
+        raise HTTPException(status_code=502, detail=f"NVIDIA OCR API error: {str(e)}")
+
+    ocr_json = response.json()
+    print("OCR Response:", ocr_json)
+
+    detections = ocr_json.get("text_detections", [])
+    if not detections:
+        data = ocr_json.get("data", [])
+        if isinstance(data, list) and len(data) > 0:
+            detections = data[0].get("text_detections", [])
+
+    lines = []
+    for det in detections:
+        text = ""
+        if isinstance(det, dict):
+            if "text_prediction" in det:
+                text = det["text_prediction"].get("text", "").strip()
+            else:
+                text = det.get("text", "").strip()
+        if text:
+            lines.append(text)
+
+    return "\n".join(lines)
+
+
+def call_llm(ocr_text: str, system_prompt: str) -> dict:
+    """Send OCR text to the LLM with the given system prompt and return parsed JSON dict."""
+    llm_payload = {
+        "model": LLM_MODEL,
+        "max_tokens": 1024,
+        "temperature": 0.2,
+        "top_p": 0.9,
+        "messages": [
+            {"role": "system", "content": system_prompt},
+            {
+                "role": "user",
+                "content": (
+                    f"Here is the OCR text extracted from the Aadhaar card:\n\n"
+                    f"{ocr_text}\n\n"
+                    f"Extract the required data and return ONLY the JSON object."
+                ),
+            },
+        ],
+    }
+
+    try:
+        llm_response = requests.post(LLM_URL, headers=LLM_HEADERS, json=llm_payload, timeout=60)
+        llm_response.raise_for_status()
+        llm_json = llm_response.json()
+        print("LLM Response JSON:", llm_json)
+    except requests.exceptions.RequestException as e:
+        raise HTTPException(status_code=502, detail=f"NVIDIA LLM API error: {str(e)}")
+
+    raw_text: str = llm_json.get("choices", [{}])[0].get("message", {}).get("content", "")
+    print("LLM Raw Text:", raw_text)
+
+    if not raw_text:
+        raise HTTPException(status_code=502, detail="LLM returned an empty response")
+
+    cleaned = re.sub(r"```json\s*", "", raw_text, flags=re.IGNORECASE)
+    cleaned = re.sub(r"```\s*", "", cleaned).strip()
+
+    parsed = None
+    try:
+        parsed = json.loads(cleaned)
+    except json.JSONDecodeError:
+        match = re.search(r"\{[\s\S]*\}", cleaned)
+        if not match:
+            raise HTTPException(
+                status_code=502,
+                detail=f"LLM did not return valid JSON. Preview: {raw_text[:400]}",
+            )
+        try:
+            parsed = json.loads(match.group(0))
+        except json.JSONDecodeError as e:
+            raise HTTPException(status_code=502, detail=f"JSON parse error: {str(e)}")
+
+    if not isinstance(parsed, dict):
+        raise HTTPException(
+            status_code=502,
+            detail=f"LLM response is not a JSON object. Got: {type(parsed).__name__}",
+        )
+
+    print("LLM Parsed Data:", parsed)
+    return parsed
+
+
+# ── Request / Response models ─────────────────────────────────────────────────
+
+class AadhaarFrontData(BaseModel):
+    name: str
+    dob: str
+    gender: str
+    aadhaar_no: str
+
+
+class AadhaarBackData(BaseModel):
+    address: str
+    village_city: str
+    state: str
+    pincode: str
+
+
+# ── Endpoints ─────────────────────────────────────────────────────────────────
+
+@app.post("/extract-front", response_model=AadhaarFrontData)
+async def extract_front(file: UploadFile = File(...)):
+    """
+    Upload the FRONT side of an Aadhaar card image.
+
+    Pipeline:
+      1. nemoretriever-ocr-v1  →  raw OCR text
+      2. nvidia-nemotron-nano-9b-v2  →  structured JSON
+
+    Returns: name, dob, gender, aadhaar_no
+    """
+    ocr_text = await run_ocr(file)
+
+    if not ocr_text.strip():
+        raise HTTPException(status_code=422, detail="OCR produced no text. Check the image quality.")
+
+    parsed = call_llm(ocr_text, FRONT_SYSTEM_PROMPT)
+
+    raw_aadhaar = str(parsed.get("aadhaar_no", ""))
+    aadhaar_digits = re.sub(r"\D", "", raw_aadhaar)
+
+    return AadhaarFrontData(
+        name=str(parsed.get("name", "")).strip()[:100],
+        dob=str(parsed.get("dob", "")).strip()[:12],
+        gender=str(parsed.get("gender", "")).strip()[:20],
+        aadhaar_no=aadhaar_digits[:12],
+    )
+
+
+@app.post("/extract-back", response_model=AadhaarBackData)
+async def extract_back(file: UploadFile = File(...)):
+    """
+    Upload the BACK side of an Aadhaar card image.
+
+    Pipeline:
+      1. nemoretriever-ocr-v1  →  raw OCR text
+      2. nvidia-nemotron-nano-9b-v2  →  structured JSON
+
+    Returns: address, village_city, state, pincode
+    """
+    ocr_text = await run_ocr(file)
+
+    if not ocr_text.strip():
+        raise HTTPException(status_code=422, detail="OCR produced no text. Check the image quality.")
+
+    parsed = call_llm(ocr_text, BACK_SYSTEM_PROMPT)
+
+    raw_pin = str(parsed.get("pincode", ""))
+    pin_digits = re.sub(r"\D", "", raw_pin)[:6]
+
+    return AadhaarBackData(
+        address=str(parsed.get("address", "")).strip()[:200],
+        village_city=str(parsed.get("village_city", "")).strip()[:100],
+        state=str(parsed.get("state", "")).strip()[:60],
+        pincode=pin_digits,
+    )
+
+
+@app.get("/health")
+async def health_check():
+    return {"status": "healthy", "model": LLM_MODEL}
+
+
+@app.get("/")
+async def root():
+    return FileResponse("index.html")
+
+
+if __name__ == "__main__":
+    import uvicorn
+    uvicorn.run("app:app", host="0.0.0.0", port=7860, reload=True)

output.json

@@ -0,0 +1,2430 @@
+{
+  "data": [
+    {
+      "index": 0,
+      "text_detections": [
+        {
+          "text_prediction": {
+            "text": "Tax Invoice",
+            "confidence": 0.9748197097921061
+          },
+          "bounding_box": {
+            "points": [
+              {
+                "x": 0.4281061589717865,
+                "y": 0.026970278471708298
+              },
+              {
+                "x": 0.5610970854759216,
+                "y": 0.026970278471708298
+              },
+              {
+                "x": 0.5610970854759216,
+                "y": 0.04100232198834419
+              },
+              {
+                "x": 0.4281061589717865,
+                "y": 0.04100232198834419
+              }
+            ]
+          }
+        },
+        {
+          "text_prediction": {
+            "text": "Ace Mobile Manufacturer Pvt Ltd B-209, Park Plaza,",
+            "confidence": 0.9503553236710961
+          },
+          "bounding_box": {
+            "points": [
+              {
+                "x": 0.15283800661563873,
+                "y": 0.07046565413475037
+              },
+              {
+                "x": 0.4736310839653015,
+                "y": 0.07046565413475037
+              },
+              {
+                "x": 0.4736310839653015,
+                "y": 0.09781374037265778
+              },
+              {
+                "x": 0.15283800661563873,
+                "y": 0.09781374037265778
+              }
+            ]
+          }
+        },
+        {
+          "text_prediction": {
+            "text": "Invoice No. ACMPLM/23-2",
+            "confidence": 0.5086731281996885
+          },
+          "bounding_box": {
+            "points": [
+              {
+                "x": 0.5034815669059753,
+                "y": 0.06732074171304703
+              },
+              {
+                "x": 0.6023612022399902,
+                "y": 0.06732074171304703
+              },
+              {
+                "x": 0.6023612022399902,
+                "y": 0.09628961980342865
+              },
+              {
+                "x": 0.5034815669059753,
+                "y": 0.09628961980342865
+              }
+            ]
+          }
+        },
+        {
+          "text_prediction": {
+            "text": "e-Way Bill No.",
+            "confidence": 0.753118366159215
+          },
+          "bounding_box": {
+            "points": [
+              {
+                "x": 0.6380387544631958,
+                "y": 0.06735839694738388
+              },
+              {
+                "x": 0.7343797087669373,
+                "y": 0.06735839694738388
+              },
+              {
+                "x": 0.7343797087669373,
+                "y": 0.07988782972097397
+              },
+              {
+                "x": 0.6380387544631958,
+                "y": 0.07988782972097397
+              }
+            ]
+          }
+        },
+        {
+          "text_prediction": {
+            "text": "Dated",
+            "confidence": 0.9807570471020685
+          },
+          "bounding_box": {
+            "points": [
+              {
+                "x": 0.7477322220802307,
+                "y": 0.06691493839025497
+              },
+              {
+                "x": 0.7990933656692505,
+                "y": 0.06691493839025497
+              },
+              {
+                "x": 0.7990933656692505,
+                "y": 0.07908544689416885
+              },
+              {
+                "x": 0.7477322220802307,
+                "y": 0.07908544689416885
+              }
+            ]
+          }
+        },
+        {
+          "text_prediction": {
+            "text": "18-Apr-2019",
+            "confidence": 0.9013753213124308
+          },
+          "bounding_box": {
+            "points": [
+              {
+                "x": 0.7535693049430847,
+                "y": 0.08511585742235184
+              },
+              {
+                "x": 0.8546010255813599,
+                "y": 0.08511585742235184
+              },
+              {
+                "x": 0.8546010255813599,
+                "y": 0.09904323518276215
+              },
+              {
+                "x": 0.7535693049430847,
+                "y": 0.09904323518276215
+              }
+            ]
+          }
+        },
+        {
+          "text_prediction": {
+            "text": "Krishna Nagar",
+            "confidence": 0.9462618103555019
+          },
+          "bounding_box": {
+            "points": [
+              {
+                "x": 0.15382921695709229,
+                "y": 0.10059942305088043
+              },
+              {
+                "x": 0.2792307138442993,
+                "y": 0.10059942305088043
+              },
+              {
+                "x": 0.2792307138442993,
+                "y": 0.11339758336544037
+              },
+              {
+                "x": 0.15382921695709229,
+                "y": 0.11339758336544037
+              }
+            ]
+          }
+        },
+        {
+          "text_prediction": {
+            "text": "Delivery Note",
+            "confidence": 0.9796860206854658
+          },
+          "bounding_box": {
+            "points": [
+              {
+                "x": 0.5049098134040833,
+                "y": 0.10229111462831497
+              },
+              {
+                "x": 0.6149916052818298,
+                "y": 0.10229111462831497
+              },
+              {
+                "x": 0.6149916052818298,
+                "y": 0.1148548349738121
+              },
+              {
+                "x": 0.5049098134040833,
+                "y": 0.1148548349738121
+              }
+            ]
+          }
+        },
+        {
+          "text_prediction": {
+            "text": "Mode/Terms of Payment",
+            "confidence": 0.9528241199171568
+          },
+          "bounding_box": {
+            "points": [
+              {
+                "x": 0.7510144710540771,
+                "y": 0.1020733043551445
+              },
+              {
+                "x": 0.9532119035720825,
+                "y": 0.1020733043551445
+              },
+              {
+                "x": 0.9532119035720825,
+                "y": 0.11538157612085342
+              },
+              {
+                "x": 0.7510144710540771,
+                "y": 0.11538157612085342
+              }
+            ]
+          }
+        },
+        {
+          "text_prediction": {
+            "text": "Lucknow",
+            "confidence": 0.9842854154065773
+          },
+          "bounding_box": {
+            "points": [
+              {
+                "x": 0.15341368317604065,
+                "y": 0.11627411842346191
+              },
+              {
+                "x": 0.23254437744617462,
+                "y": 0.11627411842346191
+              },
+              {
+                "x": 0.23254437744617462,
+                "y": 0.1261703073978424
+              },
+              {
+                "x": 0.15341368317604065,
+                "y": 0.1261703073978424
+              }
+            ]
+          }
+        },
+        {
+          "text_prediction": {
+            "text": "State Name: Uttar Pradesh, Code 009",
+            "confidence": 0.8335085842638407
+          },
+          "bounding_box": {
+            "points": [
+              {
+                "x": 0.15377135574817657,
+                "y": 0.14566902816295624
+              },
+              {
+                "x": 0.43872737884521484,
+                "y": 0.14566902816295624
+              },
+              {
+                "x": 0.43872737884521484,
+                "y": 0.15648652613162994
+              },
+              {
+                "x": 0.15377135574817657,
+                "y": 0.15648652613162994
+              }
+            ]
+          }
+        },
+        {
+          "text_prediction": {
+            "text": "GSTIN/UIN: 09AABCS1429B12S",
+            "confidence": 0.6739966742007417
+          },
+          "bounding_box": {
+            "points": [
+              {
+                "x": 0.15627622604370117,
+                "y": 0.1305101364850998
+              },
+              {
+                "x": 0.4420973062515259,
+                "y": 0.1305101364850998
+              },
+              {
+                "x": 0.4420973062515259,
+                "y": 0.14196757972240448
+              },
+              {
+                "x": 0.15627622604370117,
+                "y": 0.14196757972240448
+              }
+            ]
+          }
+        },
+        {
+          "text_prediction": {
+            "text": "Supplier's Ref.",
+            "confidence": 0.9569732677103384
+          },
+          "bounding_box": {
+            "points": [
+              {
+                "x": 0.504898726940155,
+                "y": 0.1362711638212204
+              },
+              {
+                "x": 0.6275951862335205,
+                "y": 0.1362711638212204
+              },
+              {
+                "x": 0.6275951862335205,
+                "y": 0.1503659337759018
+              },
+              {
+                "x": 0.504898726940155,
+                "y": 0.1503659337759018
+              }
+            ]
+          }
+        },
+        {
+          "text_prediction": {
+            "text": "Other Reference(s)",
+            "confidence": 0.9026041319961966
+          },
+          "bounding_box": {
+            "points": [
+              {
+                "x": 0.7479817867279053,
+                "y": 0.1368047595024109
+              },
+              {
+                "x": 0.9088143110275269,
+                "y": 0.1368047595024109
+              },
+              {
+                "x": 0.9088143110275269,
+                "y": 0.15012960135936737
+              },
+              {
+                "x": 0.7479817867279053,
+                "y": 0.15012960135936737
+              }
+            ]
+          }
+        },
+        {
+          "text_prediction": {
+            "text": "Buyer",
+            "confidence": 0.9962499376180411
+          },
+          "bounding_box": {
+            "points": [
+              {
+                "x": 0.009164477698504925,
+                "y": 0.18092162907123566
+              },
+              {
+                "x": 0.05915549024939537,
+                "y": 0.18092162907123566
+              },
+              {
+                "x": 0.05915549024939537,
+                "y": 0.19374606013298035
+              },
+              {
+                "x": 0.009164477698504925,
+                "y": 0.19374606013298035
+              }
+            ]
+          }
+        },
+        {
+          "text_prediction": {
+            "text": "E-Mail: info@ace.com",
+            "confidence": 0.8351061763417369
+          },
+          "bounding_box": {
+            "points": [
+              {
+                "x": 0.15388941764831543,
+                "y": 0.15974482893943787
+              },
+              {
+                "x": 0.3505077660083771,
+                "y": 0.15974482893943787
+              },
+              {
+                "x": 0.3505077660083771,
+                "y": 0.17221148312091827
+              },
+              {
+                "x": 0.15388941764831543,
+                "y": 0.17221148312091827
+              }
+            ]
+          }
+        },
+        {
+          "text_prediction": {
+            "text": "Buyer's Order No.",
+            "confidence": 0.9494322212094759
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+        {
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+            "text": "TaxAmount (in words) INR Ten Lakh Thirty Two Thousand Only",
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+        {
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+        {
+          "text_prediction": {
+            "text": "for Ace Mobile Manufacturer Pvt Ltd",
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+          "bounding_box": {
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+        },
+        {
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+        },
+        {
+          "text_prediction": {
+            "text": "This is a Computer Generated Invoice",
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+          },
+          "bounding_box": {
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+        }
+      ]
+    }
+  ],
+  "usage": {
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+  }
+}

requirements.txt

@@ -0,0 +1,5 @@
+fastapi
+uvicorn
+requests
+pydantic
+python-multipart