[ADD]README.md

.dockerignore

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+__pycache__
+*.pyc
+*.pyo
+*.pyd
+.env
+.venv
+.git
+.gitignore

.idea/.gitignore

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+# Default ignored files
+/shelf/
+/workspace.xml
+# Editor-based HTTP Client requests
+/httpRequests/

.idea/ai_deal_sentiment_api.iml

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+<?xml version="1.0" encoding="UTF-8"?>
+<module type="PYTHON_MODULE" version="4">
+  <component name="NewModuleRootManager">
+    <content url="file://$MODULE_DIR$">
+      <excludeFolder url="file://$MODULE_DIR$/.venv" />
+    </content>
+    <orderEntry type="inheritedJdk" />
+    <orderEntry type="sourceFolder" forTests="false" />
+  </component>
+</module>

.idea/copilot.data.migration.ask2agent.xml

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+<?xml version="1.0" encoding="UTF-8"?>
+<project version="4">
+  <component name="Ask2AgentMigrationStateService">
+    <option name="migrationStatus" value="COMPLETED" />
+  </component>
+</project>

.idea/inspectionProfiles/Project_Default.xml

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+<component name="InspectionProjectProfileManager">
+  <profile version="1.0">
+    <option name="myName" value="Project Default" />
+    <inspection_tool class="PyPackageRequirementsInspection" enabled="true" level="WARNING" enabled_by_default="true">
+      <option name="ignoredPackages">
+        <list>
+          <option value="fastapi" />
+          <option value="uvicorn" />
+          <option value="transformers" />
+          <option value="torch" />
+          <option value="pypdf" />
+          <option value="python-docx" />
+          <option value="python-multipart" />
+          <option value="requests" />
+          <option value="python-dotenv" />
+        </list>
+      </option>
+    </inspection_tool>
+  </profile>
+</component>

.idea/inspectionProfiles/profiles_settings.xml

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+<component name="InspectionProjectProfileManager">
+  <settings>
+    <option name="USE_PROJECT_PROFILE" value="false" />
+    <version value="1.0" />
+  </settings>
+</component>

.idea/modules.xml

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+<?xml version="1.0" encoding="UTF-8"?>
+<project version="4">
+  <component name="ProjectModuleManager">
+    <modules>
+      <module fileurl="file://$PROJECT_DIR$/.idea/ai_deal_sentiment_api.iml" filepath="$PROJECT_DIR$/.idea/ai_deal_sentiment_api.iml" />
+    </modules>
+  </component>
+</project>

Dockerfile

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+FROM python:3.10-slim
+
+WORKDIR /app
+
+COPY requirements.txt /app/requirements.txt
+RUN apt-get update && apt-get install -y git && rm -rf /var/lib/apt/lists/*
+RUN pip install --no-cache-dir -r /app/requirements.txt
+
+COPY app /app/app
+COPY templates /app/templates
+COPY static /app/static
+
+ENV PORT=7860
+EXPOSE 7860
+
+CMD ["uvicorn", "app.main:app", "--host", "0.0.0.0", "--port", "7860"]

README.md

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 ---
 title: AI Deal Sentiment API
+<<<<<<< HEAD
 emoji: 🐨
 colorFrom: gray
 colorTo: purple
@@ -8,3 +9,62 @@ pinned: false
 ---
 
 Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
+=======
+emoji: 🤖
+colorFrom: gray
+colorTo: blue
+sdk: docker
+pinned: false
+short_description: Sentiment API with a simple web UI
+---
+
+# AI Deal Sentiment API
+
+A FastAPI service that analyzes customer chat/email sentiment and returns a label, score, and brief reason. Includes a simple web UI.
+
+## Features
+- `/sentiment` API for positive/neutral/negative detection
+- `/health` for readiness checks
+- Simple web UI at `/`
+
+## Run (Local)
+
+```bash
+python -m venv .venv
+. .venv/bin/activate
+pip install -r requirements.txt
+uvicorn app.main:app --host 0.0.0.0 --port 7860
+```
+
+## Run (Docker)
+
+```bash
+docker build -t ai-deal-sentiment-api .
+docker run -p 7860:7860 ai-deal-sentiment-api
+```
+
+## API
+
+### POST `/sentiment`
+
+```json
+{
+  "text": "Customer message here"
+}
+```
+
+Response:
+```json
+{
+  "label": "positive",
+  "score": 0.82,
+  "brief_reason": "Customer intent to buy detected"
+}
+```
+
+### GET `/health`
+Returns:
+```json
+{ "status": "ok" }
+```
+>>>>>>> ff9fb03 ([ADD]Initial Commit)

app/main.py

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+from functools import lru_cache
+import os
+
+from fastapi import FastAPI, HTTPException, Request
+from fastapi.responses import HTMLResponse
+from fastapi.staticfiles import StaticFiles
+from fastapi.templating import Jinja2Templates
+from pydantic import BaseModel, Field
+import re
+from transformers import AutoModelForSequenceClassification, AutoTokenizer, pipeline
+
+app = FastAPI(title="AI Deal Sentiment API")
+app.mount("/static", StaticFiles(directory="static"), name="static")
+templates = Jinja2Templates(directory="templates")
+
+MODEL_NAME = os.getenv("SENTIMENT_MODEL", "IberaSoft/customer-sentiment-analyzer")
+
+
+class SentimentRequest(BaseModel):
+    text: str = Field(..., min_length=1)
+
+
+class SentimentResponse(BaseModel):
+    label: str
+    score: float
+    brief_reason: str
+
+
+@lru_cache(maxsize=1)
+def get_sentiment_pipeline():
+    tokenizer = AutoTokenizer.from_pretrained(MODEL_NAME)
+    model = AutoModelForSequenceClassification.from_pretrained(MODEL_NAME)
+    return pipeline("text-classification", model=model, tokenizer=tokenizer)
+
+
+@app.get("/health")
+def health() -> dict:
+    return {"status": "ok"}
+
+
+@app.get("/", response_class=HTMLResponse)
+def root(request: Request) -> HTMLResponse:
+    return templates.TemplateResponse("index.html", {"request": request})
+
+
+@app.post("/sentiment", response_model=SentimentResponse)
+def sentiment(payload: SentimentRequest) -> SentimentResponse:
+    if not payload.text.strip():
+        return SentimentResponse(label="neutral", score=0.0, brief_reason="No customer chat to assess")
+
+    classifier = get_sentiment_pipeline()
+    result = classifier(payload.text[:4000])
+    print("result:", result)
+    if not result:
+        raise HTTPException(status_code=500, detail="Sentiment model returned no result")
+
+    data = result[0]
+    label = (data.get("label") or "neutral").lower()
+    score = float(data.get("score", 0.0))
+
+    normalized = payload.text.lower()
+    normalized = normalized.replace("’", "'")
+    normalized = re.sub(r"[^a-z0-9\\s']", " ", normalized)
+    normalized = re.sub(r"\\s+", " ", normalized).strip()
+
+    disinterest = [
+        "not interested",
+        "dont want to buy",
+        "don't want to buy",
+        "do not want to buy",
+        "wont buy",
+        "won't buy",
+        "not good",
+    ]
+    positive_intent = [
+        "interested",
+        "want to buy",
+        "would like to buy",
+        "ready to buy",
+        "buy in cash",
+        "purchase",
+        "proceed",
+    ]
+
+    if any(k in normalized for k in disinterest):
+        return SentimentResponse(label="negative", score=1.0, brief_reason="Explicit disinterest from customer")
+    if any(k in normalized for k in positive_intent):
+        return SentimentResponse(label="positive", score=max(score, 0.7), brief_reason="Customer intent to buy detected")
+
+    if label == "negative" and score >= 0.6:
+        reason = f"Negative sentiment detected (score {score:.2f})"
+    elif label == "positive" and score >= 0.6:
+        reason = f"Positive sentiment detected (score {score:.2f})"
+    else:
+        reason = f"Neutral sentiment (score {score:.2f})"
+
+    return SentimentResponse(label=label, score=score, brief_reason=reason)

requirements.txt

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+fastapi==0.115.8
+uvicorn==0.30.6
+transformers==4.44.2
+torch==2.4.1
+jinja2==3.1.4

static/main.js

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+const inputEl = document.getElementById("inputText");
+const analyzeBtn = document.getElementById("analyzeBtn");
+const statusEl = document.getElementById("status");
+const resultEl = document.getElementById("result");
+const labelEl = document.getElementById("label");
+const scoreEl = document.getElementById("score");
+const reasonEl = document.getElementById("reason");
+
+async function analyze() {
+  const text = inputEl.value.trim();
+  if (!text) {
+    statusEl.textContent = "Please paste some text.";
+    return;
+  }
+  statusEl.textContent = "Analyzing...";
+  analyzeBtn.disabled = true;
+  try {
+    const res = await fetch("/sentiment", {
+      method: "POST",
+      headers: { "Content-Type": "application/json" },
+      body: JSON.stringify({ text }),
+    });
+    const data = await res.json();
+    if (!res.ok) {
+      statusEl.textContent = data.detail || "Request failed";
+      return;
+    }
+    labelEl.textContent = `Label: ${data.label}`;
+    scoreEl.textContent = `Score: ${data.score}`;
+    reasonEl.textContent = data.brief_reason || "";
+    resultEl.style.display = "block";
+    statusEl.textContent = "";
+  } catch (err) {
+    statusEl.textContent = "Network error.";
+  } finally {
+    analyzeBtn.disabled = false;
+  }
+}
+
+analyzeBtn.addEventListener("click", analyze);

static/style.css

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+:root {
+  --bg: #0f1115;
+  --panel: #171a21;
+  --text: #f5f7fb;
+  --muted: #a7b0c0;
+  --accent: #59f2a8;
+  --accent-2: #5ac8ff;
+  --border: #2a303c;
+}
+* { box-sizing: border-box; }
+body {
+  margin: 0;
+  font-family: "Space Grotesk", "Segoe UI", sans-serif;
+  background: radial-gradient(1200px 800px at 10% 10%, #1a2030, #0f1115);
+  color: var(--text);
+}
+.wrap {
+  max-width: 900px;
+  margin: 0 auto;
+  padding: 32px 20px 48px;
+}
+header { margin-bottom: 16px; }
+h1 { margin: 0 0 8px 0; font-size: 28px; }
+.lead { margin: 0; color: var(--muted); }
+textarea {
+  width: 100%;
+  min-height: 200px;
+  padding: 12px;
+  border: 1px solid var(--border);
+  background: var(--panel);
+  color: var(--text);
+  border-radius: 10px;
+  resize: vertical;
+  font-size: 14px;
+  line-height: 1.4;
+}
+.actions { display: flex; gap: 12px; margin-top: 12px; align-items: center; }
+button {
+  background: linear-gradient(120deg, var(--accent), var(--accent-2));
+  border: none;
+  color: #0b0f16;
+  font-weight: 700;
+  padding: 10px 16px;
+  border-radius: 10px;
+  cursor: pointer;
+}
+.panel {
+  margin-top: 16px;
+  padding: 16px;
+  background: var(--panel);
+  border: 1px solid var(--border);
+  border-radius: 12px;
+}
+.score { font-size: 20px; font-weight: 700; }
+.muted { color: var(--muted); }

templates/index.html

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+<!doctype html>
+<html lang="en">
+<head>
+  <meta charset="utf-8" />
+  <meta name="viewport" content="width=device-width, initial-scale=1" />
+  <title>AI Deal Sentiment</title>
+  <link rel="stylesheet" href="/static/style.css" />
+</head>
+<body>
+  <div class="wrap">
+    <header>
+      <h1>AI Deal Sentiment</h1>
+      <p class="lead">Paste customer chat or email text to get sentiment.</p>
+    </header>
+
+    <textarea id="inputText" placeholder="Paste customer chat here..."></textarea>
+    <div class="actions">
+      <button id="analyzeBtn">Analyze</button>
+      <span id="status" class="muted"></span>
+    </div>
+
+    <div id="result" class="panel" style="display:none;">
+      <div class="score" id="label">Label: --</div>
+      <p id="score" class="muted"></p>
+      <p id="reason"></p>
+    </div>
+  </div>
+
+  <script src="/static/main.js"></script>
+</body>
+</html>