Update api/views.py

api/views.py

@@ -1,215 +1,215 @@
-from django.shortcuts import render, redirect
-from django.http import HttpResponse, JsonResponse, StreamingHttpResponse
-import requests
-import uuid
-import json
-import os
-from pdf2image import convert_from_path, convert_from_bytes
-from django.views.decorators.csrf import csrf_exempt
-from django.core.files.storage import FileSystemStorage
-import threading
-import random
-import google.generativeai as genai
-import google.ai.generativelanguage as glm
-import os
-from .models import UseCases, DocumentTypes
-
-
-os.environ["_BARD_API_KEY"] = "WwhMDr8qCuXIPs1pwR-lOgPsB51q86WuevPCH5VpcCcxVEEQqywHYShAfkE19lCRXUOQaQ."
-# host_url = "http://16.170.244.54"
-host_url = "https://qbh39rzw-8000.euw.devtunnels.ms"
-googleAPIKey = "AIzaSyBeo4NGA__U6Xxy-aBE6yFm19pgq8TY-TM"
-genai.configure(api_key='AIzaSyALFCivW9GP25mbxL3W7Fv6u7m2ZHVlC8w')
-
-
-def getAnswer(images):
-    url = "https://content-vision.googleapis.com/v1/images:annotate?alt=json&key="+googleAPIKey
-    payload = {"requests": []}
-    for i in images:
-        temp = {
-            "image": {"source": {"imageUri": i}},
-            "features": [
-                {
-                    "type": "DOCUMENT_TEXT_DETECTION",
-                    "maxResults": 50,
-                    "model": "builtin/latest",
-                }
-            ],
-        }
-        payload["requests"].append(temp)
-
-    headers = {
-        "authority": "content-vision.googleapis.com",
-        "accept": "*/*",
-        "accept-language": "en-US,en;q=0.9,gu;q=0.8",
-        "content-type": "application/json",
-        "origin": "https://content-vision.googleapis.com",
-        "user-agent": "Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/111.0.0.0 Safari/537.36",
-        "x-origin": "https://explorer.apis.google.com",
-        "x-requested-with": "XMLHttpRequest",
-    }
-    response = requests.request("POST", url, headers=headers, data=json.dumps(payload))
-    OCRString = ""
-    try:
-        for i in range(len(response.json()["responses"])):
-            OCRString += "\n\n\n"+response.json()["responses"][i]["fullTextAnnotation"]["text"]
-    except Exception as e:
-        print(e)
-    return OCRString
-
-
-@csrf_exempt
-def dataExtract(request, link):
-    if request.method == "POST":
-        documentData = DocumentTypes.objects.filter(url=link).first()
-        pdf_file = request.FILES["pdf"]
-        randomUUID = str(uuid.uuid4())
-        fs = FileSystemStorage(location="static/pdf/")
-        filename = fs.save(f"{randomUUID}.pdf", pdf_file)
-        os.mkdir(f"./static/pages/{randomUUID}")
-        image_list = []
-        images = convert_from_path(
-            f"./static/pdf/{randomUUID}.pdf",
-            dpi=150,
-            output_folder=f"./static/pages/{randomUUID}",
-            fmt="png",
-            output_file=f"image",
-            thread_count=5,
-            poppler_path="./poppler-23.05.0/Library/bin/"
-        )
-        for filename in os.listdir(f"./static/pages/{randomUUID}"):
-            image_list.append(f"/static/pages/{randomUUID}/{filename}")
-        image_Array = []
-        for i in range(len(image_list)):
-            image_Array.append(host_url + image_list[i])
-
-        OCRString = getAnswer(image_Array)
-        with open(f"./OCR.txt", "w", encoding="utf-8") as f:
-            f.write(OCRString)
-        fields = documentData.fields
-        properties = {}
-        for field in fields:
-            properties[field] = {'type_': 'STRING'}
-        entityTool = {
-            'function_declarations': [
-                {
-                    'name': 'entityTool',
-                    'description': 'List of entities and value extracted from the text.',
-                    'parameters': {
-                        'type_': 'OBJECT',
-                        'properties': properties,
-                        'required': []
-                    }
-                }
-            ]
-        }
-        safety_settings = [
-            {
-                "category": "HARM_CATEGORY_HARASSMENT",
-                "threshold": "BLOCK_NONE"
-            },
-            {
-                "category": "HARM_CATEGORY_HATE_SPEECH",
-                "threshold": "BLOCK_NONE"
-            },
-            {
-                "category": "HARM_CATEGORY_SEXUALLY_EXPLICIT",
-                "threshold": "BLOCK_NONE"
-            },
-            {
-                "category": "HARM_CATEGORY_DANGEROUS_CONTENT",
-                "threshold": "BLOCK_NONE"
-            },
-        ]
-
-        model = genai.GenerativeModel(model_name='gemini-1.5-pro-latest', tools=entityTool, safety_settings=safety_settings)
-        chat = model.start_chat()
-        response = chat.send_message('PDF Data : \n\n'+OCRString)
-        fc = response.candidates[0].content.parts[0].function_call
-        data = {}
-        if fc.name == "entityTool":
-            for field in fields:
-                try:
-                    data[field] = fc.args[field]
-                except:
-                    pass
-
-        print(data)
-        return HttpResponse(
-            json.dumps({"images": image_list, "data": data}), content_type="application/json"
-        )
-    else:
-        return HttpResponse("Error")
-
-
-@csrf_exempt
-def imageToText(request):
-    if request.method == "POST":
-        jsonData = json.loads(request.body)
-        imageArr = jsonData["images"]
-        for i in range(len(imageArr)):
-            imageArr[i] = host_url + imageArr[i]
-        imageArr = [imageArr[i: i + 10] for i in range(0, len(imageArr), 10)]
-        answers = []
-        text = []
-        box = []
-        for i in range(len(imageArr)):
-            textTemp, boxTemp = getAnswer(imageArr[i])
-            text.extend(textTemp)
-            box.extend(boxTemp)
-        return HttpResponse(
-            json.dumps({"text": text, "box": box}), content_type="application/json"
-        )
-    else:
-        return HttpResponse("Error")
-
-
-def documentAIData(request):
-    usecases = UseCases.objects.all()
-    documentTypes = DocumentTypes.objects.all()
-    usecasesArr = []
-    documentTypesArr = []
-    for doc in documentTypes:
-        temp = {}
-        temp["img"] = doc.img
-        temp["name"] = doc.name
-        temp["url"] = doc.url
-        temp["usecases"] = []
-        for usecase in doc.usecases.all():
-            temp["usecases"].append(usecase.heading)
-        documentTypesArr.append(temp)
-    for usecase in usecases:
-        usecasesArr.append(usecase.heading)
-    return HttpResponse(json.dumps({"usecases": usecasesArr, "docTypes": documentTypesArr}), content_type="application/json")
-
-
-def docPages(request, link):
-    documentData = DocumentTypes.objects.filter(url=link).first()
-    usecases = documentData.usecases.all()
-    data = {
-        "title": documentData.title,
-        "name": documentData.name,
-        "subtitle": documentData.subtitle,
-        "img": documentData.img,
-        "usecases": [],
-        "fields": documentData.fields,
-        "url": documentData.url,
-    }
-    for usecase in usecases:
-        data["usecases"].append({"heading": usecase.heading, "paragraph": usecase.paragraph})
-
-    return HttpResponse(json.dumps(data), content_type="application/json")
-
-
-def edditor(request):
-    with open("./api/nanonetProducts2.json", "r") as f:
-        data = json.load(f)
-    documents = DocumentTypes.objects.all()
-    for doc in documents:
-        for entry in data:
-            if entry["name"] == doc.name:
-                doc.url = entry["link"]
-                doc.save()
-                print(doc.name, "Updated", entry["link"])
-                break
-    return HttpResponse("Hello World")
+from django.shortcuts import render, redirect
+from django.http import HttpResponse, JsonResponse, StreamingHttpResponse
+import requests
+import uuid
+import json
+import os
+from pdf2image import convert_from_path, convert_from_bytes
+from django.views.decorators.csrf import csrf_exempt
+from django.core.files.storage import FileSystemStorage
+import threading
+import random
+import google.generativeai as genai
+import google.ai.generativelanguage as glm
+import os
+from .models import UseCases, DocumentTypes
+
+
+
+# host_url = "http://16.170.244.54"
+host_url = "https://thejagstudio-absoluteai.hf.space/"
+googleAPIKey = "AIzaSyBeo4NGA__U6Xxy-aBE6yFm19pgq8TY-TM"
+genai.configure(api_key='AIzaSyALFCivW9GP25mbxL3W7Fv6u7m2ZHVlC8w')
+
+
+def getAnswer(images):
+    url = "https://content-vision.googleapis.com/v1/images:annotate?alt=json&key="+googleAPIKey
+    payload = {"requests": []}
+    for i in images:
+        temp = {
+            "image": {"source": {"imageUri": i}},
+            "features": [
+                {
+                    "type": "DOCUMENT_TEXT_DETECTION",
+                    "maxResults": 50,
+                    "model": "builtin/latest",
+                }
+            ],
+        }
+        payload["requests"].append(temp)
+
+    headers = {
+        "authority": "content-vision.googleapis.com",
+        "accept": "*/*",
+        "accept-language": "en-US,en;q=0.9,gu;q=0.8",
+        "content-type": "application/json",
+        "origin": "https://content-vision.googleapis.com",
+        "user-agent": "Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/111.0.0.0 Safari/537.36",
+        "x-origin": "https://explorer.apis.google.com",
+        "x-requested-with": "XMLHttpRequest",
+    }
+    response = requests.request("POST", url, headers=headers, data=json.dumps(payload))
+    OCRString = ""
+    try:
+        for i in range(len(response.json()["responses"])):
+            OCRString += "\n\n\n"+response.json()["responses"][i]["fullTextAnnotation"]["text"]
+    except Exception as e:
+        print(e)
+    return OCRString
+
+
+@csrf_exempt
+def dataExtract(request, link):
+    if request.method == "POST":
+        documentData = DocumentTypes.objects.filter(url=link).first()
+        pdf_file = request.FILES["pdf"]
+        randomUUID = str(uuid.uuid4())
+        fs = FileSystemStorage(location="static/pdf/")
+        filename = fs.save(f"{randomUUID}.pdf", pdf_file)
+        os.mkdir(f"./static/pages/{randomUUID}")
+        image_list = []
+        images = convert_from_path(
+            f"./static/pdf/{randomUUID}.pdf",
+            dpi=150,
+            output_folder=f"./static/pages/{randomUUID}",
+            fmt="png",
+            output_file=f"image",
+            thread_count=5,
+            poppler_path="./poppler-23.05.0/Library/bin/"
+        )
+        for filename in os.listdir(f"./static/pages/{randomUUID}"):
+            image_list.append(f"/static/pages/{randomUUID}/{filename}")
+        image_Array = []
+        for i in range(len(image_list)):
+            image_Array.append(host_url + image_list[i])
+
+        OCRString = getAnswer(image_Array)
+        with open(f"./OCR.txt", "w", encoding="utf-8") as f:
+            f.write(OCRString)
+        fields = documentData.fields
+        properties = {}
+        for field in fields:
+            properties[field] = {'type_': 'STRING'}
+        entityTool = {
+            'function_declarations': [
+                {
+                    'name': 'entityTool',
+                    'description': 'List of entities and value extracted from the text.',
+                    'parameters': {
+                        'type_': 'OBJECT',
+                        'properties': properties,
+                        'required': []
+                    }
+                }
+            ]
+        }
+        safety_settings = [
+            {
+                "category": "HARM_CATEGORY_HARASSMENT",
+                "threshold": "BLOCK_NONE"
+            },
+            {
+                "category": "HARM_CATEGORY_HATE_SPEECH",
+                "threshold": "BLOCK_NONE"
+            },
+            {
+                "category": "HARM_CATEGORY_SEXUALLY_EXPLICIT",
+                "threshold": "BLOCK_NONE"
+            },
+            {
+                "category": "HARM_CATEGORY_DANGEROUS_CONTENT",
+                "threshold": "BLOCK_NONE"
+            },
+        ]
+
+        model = genai.GenerativeModel(model_name='gemini-1.5-pro-latest', tools=entityTool, safety_settings=safety_settings)
+        chat = model.start_chat()
+        response = chat.send_message('PDF Data : \n\n'+OCRString)
+        fc = response.candidates[0].content.parts[0].function_call
+        data = {}
+        if fc.name == "entityTool":
+            for field in fields:
+                try:
+                    data[field] = fc.args[field]
+                except:
+                    pass
+
+        print(data)
+        return HttpResponse(
+            json.dumps({"images": image_list, "data": data}), content_type="application/json"
+        )
+    else:
+        return HttpResponse("Error")
+
+
+@csrf_exempt
+def imageToText(request):
+    if request.method == "POST":
+        jsonData = json.loads(request.body)
+        imageArr = jsonData["images"]
+        for i in range(len(imageArr)):
+            imageArr[i] = host_url + imageArr[i]
+        imageArr = [imageArr[i: i + 10] for i in range(0, len(imageArr), 10)]
+        answers = []
+        text = []
+        box = []
+        for i in range(len(imageArr)):
+            textTemp, boxTemp = getAnswer(imageArr[i])
+            text.extend(textTemp)
+            box.extend(boxTemp)
+        return HttpResponse(
+            json.dumps({"text": text, "box": box}), content_type="application/json"
+        )
+    else:
+        return HttpResponse("Error")
+
+
+def documentAIData(request):
+    usecases = UseCases.objects.all()
+    documentTypes = DocumentTypes.objects.all()
+    usecasesArr = []
+    documentTypesArr = []
+    for doc in documentTypes:
+        temp = {}
+        temp["img"] = doc.img
+        temp["name"] = doc.name
+        temp["url"] = doc.url
+        temp["usecases"] = []
+        for usecase in doc.usecases.all():
+            temp["usecases"].append(usecase.heading)
+        documentTypesArr.append(temp)
+    for usecase in usecases:
+        usecasesArr.append(usecase.heading)
+    return HttpResponse(json.dumps({"usecases": usecasesArr, "docTypes": documentTypesArr}), content_type="application/json")
+
+
+def docPages(request, link):
+    documentData = DocumentTypes.objects.filter(url=link).first()
+    usecases = documentData.usecases.all()
+    data = {
+        "title": documentData.title,
+        "name": documentData.name,
+        "subtitle": documentData.subtitle,
+        "img": documentData.img,
+        "usecases": [],
+        "fields": documentData.fields,
+        "url": documentData.url,
+    }
+    for usecase in usecases:
+        data["usecases"].append({"heading": usecase.heading, "paragraph": usecase.paragraph})
+
+    return HttpResponse(json.dumps(data), content_type="application/json")
+
+
+def edditor(request):
+    with open("./api/nanonetProducts2.json", "r") as f:
+        data = json.load(f)
+    documents = DocumentTypes.objects.all()
+    for doc in documents:
+        for entry in data:
+            if entry["name"] == doc.name:
+                doc.url = entry["link"]
+                doc.save()
+                print(doc.name, "Updated", entry["link"])
+                break
+    return HttpResponse("Hello World")