gradio setup

Dockerfile

@@ -1,29 +1,52 @@
-# Dockerfile
+# # Dockerfile
+# FROM python:3.10-slim
+
+# WORKDIR /app
+
+# # Install system dependencies
+# RUN apt-get update && apt-get install -y git-lfs && apt-get clean
+
+# # Install Python dependencies
+# COPY requirements.txt .
+# RUN pip install --no-cache-dir -r requirements.txt && \
+#     python -m spacy download en_core_web_sm
+
+# # Copy application code and model
+# COPY main.py .
+# COPY config.json .
+# COPY model.safetensors .
+# COPY tokenizer_config.json .
+# COPY vocab.json .
+# COPY merges.txt .
+# COPY generation_config.json .
+# COPY special_tokens_map.json .
+
+# # Fix permissions
+# RUN chmod -R 755 /app
+
+# EXPOSE 7860
+
+# CMD ["uvicorn", "main:app", "--host", "0.0.0.0", "--port", "7860"]
+
+
 FROM python:3.10-slim
 
 WORKDIR /app
 
 # Install system dependencies
-RUN apt-get update && apt-get install -y git-lfs && apt-get clean
+RUN apt-get update && apt-get install -y git-lfs && apt-get clean && rm -rf /var/lib/apt/lists/*
 
-# Install Python dependencies
+# Copy requirements and install Python dependencies
 COPY requirements.txt .
-RUN pip install --no-cache-dir -r requirements.txt && \
+RUN pip install --upgrade pip && \
+    pip install --no-cache-dir -r requirements.txt && \
     python -m spacy download en_core_web_sm
 
-# Copy application code and model
-COPY main.py .
-COPY config.json .
-COPY model.safetensors .
-COPY tokenizer_config.json .
-COPY vocab.json .
-COPY merges.txt .
-COPY generation_config.json .
-COPY special_tokens_map.json .
-
-# Fix permissions
-RUN chmod -R 755 /app
+# Copy all application files
+COPY . .
 
+# Expose port for Gradio
 EXPOSE 7860
 
-CMD ["uvicorn", "main:app", "--host", "0.0.0.0", "--port", "7860"]
+# Command to run the Gradio app
+CMD ["python", "app.py"]

app.py

@@ -0,0 +1,100 @@
+import gradio as gr
+from transformers import GPT2LMHeadModel, GPT2Tokenizer
+import torch
+import spacy
+
+# Load models
+model = GPT2LMHeadModel.from_pretrained("./")
+tokenizer = GPT2Tokenizer.from_pretrained("./")
+tokenizer.pad_token = tokenizer.eos_token
+
+try:
+    nlp = spacy.load("en_core_web_sm")
+except OSError:
+    import spacy.cli
+    spacy.cli.download("en_core_web_sm")
+    nlp = spacy.load("en_core_web_sm")
+
+def summarize_description(text):
+    doc = nlp(text)
+    keywords = [token.text for token in doc if token.pos_ in ["NOUN", "PROPN", "ADJ"]]
+    return " ".join(keywords[:12])
+
+def generate_slogans(brand, description, industry, tone="playful", num=5, liked_slogan=None):
+    processed_desc = summarize_description(description)
+    if liked_slogan:
+        prompt1 = (
+            f"Create {industry} brand slogans similar to: '{liked_slogan}'\n"
+            f"Brand: {brand}\n"
+            f"Key Attributes: {processed_desc}\n"
+            "Slogan:"
+        )
+        prompt2 = (
+            f"Generate slogans in the style of: '{liked_slogan}'\n"
+            f"For: {brand}\n"
+            f"Details: {processed_desc}\n"
+            "Slogan:"
+        )
+    else:
+        prompt1 = (
+            f"Create a {industry} brand slogan that's {tone} and unique.\n"
+            f"Brand: {brand}\n"
+            f"Attributes: {processed_desc}\n"
+            "Slogan:"
+        )
+        prompt2 = (
+            f"Write {tone} marketing slogans for this {industry} brand:\n"
+            f"Name: {brand}\n"
+            f"About: {processed_desc}\n"
+            "Slogan:"
+        )
+
+    tone_presets = {
+        "playful": {"temperature": 0.95, "top_p": 0.95, "repetition_penalty": 1.2},
+        "bold": {"temperature": 0.8, "top_p": 0.9, "repetition_penalty": 1.45},
+        "minimalist": {"temperature": 0.6, "top_p": 0.8, "repetition_penalty": 1.5},
+        "luxury": {"temperature": 0.7, "top_p": 0.85, "repetition_penalty": 1.35},
+        "classic": {"temperature": 0.7, "top_p": 0.9, "repetition_penalty": 1.25}
+    }
+
+    gen_params = {
+        **tone_presets[tone],
+        "max_new_tokens": 25,
+        "num_return_sequences": num,
+        "do_sample": True,
+        "pad_token_id": tokenizer.eos_token_id
+    }
+
+    outputs1 = model.generate(**tokenizer(prompt1, return_tensors="pt"), **gen_params)
+    outputs2 = model.generate(**tokenizer(prompt2, return_tensors="pt"), **gen_params)
+
+    slogans = []
+    for outputs in [outputs1, outputs2]:
+        for output in outputs:
+            raw = tokenizer.decode(output, skip_special_tokens=True)
+            clean = raw.split("Slogan:")[-1].strip()
+            clean = clean.split("\n")[0].replace('"', '').replace('(', '').split(".")[0].strip()
+            if len(clean) > 4 and clean not in slogans:
+                slogans.append(clean)
+
+    return {"slogans": slogans[:num * 2]}
+
+# Minimal Gradio interface (hidden)
+with gr.Blocks() as demo:
+    gr.Markdown("API only. No interface required.")
+    # Add an API route
+    gr.api(
+        generate_slogans,
+        api_name="predict",
+        inputs=[
+            gr.Textbox(label="Brand"),
+            gr.Textbox(label="Description"),
+            gr.Textbox(label="Industry"),
+            gr.Dropdown(["playful", "bold", "minimalist", "luxury", "classic"], label="Tone", value="playful"),
+            gr.Slider(1, 10, value=5, label="Number of Slogans"),
+            gr.Textbox(label="Generate like this slogan (optional)", value=None)
+        ],
+        outputs=gr.JSON(label="Slogans")
+    )
+
+demo.launch(show_api=True)

requirements.txt

@@ -1,4 +1,5 @@
 # requirements.txt
+gradio>=4.0.0
 fastapi>=0.68.0
 uvicorn>=0.15.0
 transformers>=4.0.0