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.env

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+GOOGLE_API_KEY

.gitattributes

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Dockerfile

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+FROM python:3.9
+
+WORKDIR /code
+
+COPY ./requirements.txt /code/requirements.txt
+
+RUN pip install --no-cache-dir --upgrade -r /code/requirements.txt
+
+COPY . .
+
+CMD ["uvicorn", "main:app", "--host", "0.0.0.0", "--port", "7860"]

README.md

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----
-title: Table
-emoji: 🌍
-colorFrom: purple
-colorTo: gray
-sdk: docker
-pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
+---
+title: Backend
+emoji: 📊
+colorFrom: blue
+colorTo: gray
+sdk: docker
+pinned: false
+---
+
+Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

main.py

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+from fastapi import FastAPI, HTTPException 
+from fastapi.middleware.cors import CORSMiddleware
+import google.generativeai as genai
+import os
+import requests
+import pandas as pd
+import validators
+from sklearn.feature_extraction.text import TfidfVectorizer
+
+from dotenv import load_dotenv
+load_dotenv()
+genai.configure(api_key=os.getenv("GOOGLE_API_KEY"))
+df=pd.read_csv("salaries.csv")
+
+app = FastAPI()
+
+origins=[
+    "http://localhost:5173",
+    
+]
+
+app.add_middleware(
+    CORSMiddleware,
+    allow_origins=origins,
+    allow_credentials=True,
+    allow_methods=['*'],
+    allow_headers=['*']
+)
+
+model=genai.GenerativeModel("gemini-1.5-flash")
+
+
+df_combined = df.astype(str).apply(lambda x: ' '.join(x), axis=1).tolist()
+
+vectorizer = TfidfVectorizer()
+X = vectorizer.fit_transform(df_combined)
+
+def genetate_gemini_content(prompt,content):
+    response=model.generate_content([prompt,content])
+    return response.text
+ 
+ 
+work_year = df['work_year']
+job_titles = df['job_title']
+salaries = df['salary']
+experience_level = df['experience_level']
+employment_type = df['employment_type']
+salary_in_usd = df['salary_in_usd']
+company_size = df['company_size']
+
+# Combine all columns as strings for vectorization (if needed)
+df_combined = df.astype(str).apply(lambda x: ' '.join(x), axis=1).tolist()
+
+# Use TF-IDF Vectorizer for embeddings
+vectorizer = TfidfVectorizer()
+X = vectorizer.fit_transform(df_combined)
+
+prompt = f"""
+
+I have a dataset with the following salary information:
+
+Work Year: {work_year.tolist()}
+Job Titles: {job_titles.tolist()}
+Salaries (in USD): {salary_in_usd.tolist()}
+Experience Level: {experience_level.tolist()}
+Employment Type: {employment_type.tolist()}
+Company Size: {company_size.tolist()}
+
+Based on this data, can you answer the following question:
+ 
+
+Please provide a short and direct answer based on the data. No extra explanations are needed, just the answer less than 2 lines and dont use "\ n" or  any special charcher other than related to the data .
+"""
+
+@app.get("/hi")
+async def ee():
+    return {"de":"hehe"}
+
+@app.post("/webqa")
+async def webchat(question:str):
+    try:
+        response=model.generate_content([prompt,question])
+        print(response.text)
+        return response.text 
+       
+    except:
+        raise HTTPException(status_code=500, detail="Internal Server Error")
+        
+

requirements.txt

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+
+google-generativeai
+python_dotenv
+fastapi
+uvicorn
+requests
+validators
+scikit-learn
+pandas