Update app.py

app.py

@@ -1,175 +1 @@
-import gradio as gr
-import requests
-
-NL = chr(10)
-
-# Map user-facing modality choices to HF Hub task tags
-TASK_MAP = {
-    "Text in -> text out": ["text-generation", "text2text-generation"],
-    "Images in (docs, UI, scans)": ["image-to-text", "visual-question-answering"],
-    "Audio in (speech)": ["automatic-speech-recognition"],
-    "Structured data (tables, logs)": ["tabular-classification", "tabular-regression"],
-    "Code": ["text-generation"],
-}
-
-LICENSE_FILTER = {
-    "Standard SaaS API is fine": None,
-    "Must stay in a specific cloud region": None,
-    "Strict: prefer on-prem / VPC only": "apache-2.0",
-}
-
-def fetch_top_models(tasks, license_filter=None, top_n=5):
-    seen = set()
-    results = []
-    for task in tasks:
-        try:
-            params = {
-                "pipeline_tag": task,
-                "sort": "downloads",
-                "direction": "-1",
-                "limit": "20",
-            }
-            if license_filter:
-                params["license"] = license_filter
-            resp = requests.get(
-                "https://huggingface.co/api/models",
-                params=params,
-                timeout=15,
-            )
-            if resp.status_code == 200:
-                data = resp.json()
-                for m in data:
-                    mid = m.get("modelId", m.get("id", ""))
-                    if mid and mid not in seen:
-                        seen.add(mid)
-                        downloads = m.get("downloads", 0) or 0
-                        likes = m.get("likes", 0) or 0
-                        results.append((mid, task, downloads, likes))
-                        if len(results) >= top_n * len(tasks):
-                            break
-        except Exception:
-            pass
-    results.sort(key=lambda x: x[2], reverse=True)
-    return results[:top_n]
-
-def format_model_table(models):
-    if not models:
-        return "No models found via HF API. Please try again."
-    header = "| # | Model | Task | Downloads | Likes |" + NL
-    header += "|---|-------|------|-----------|-------|" + NL
-    rows = ""
-    for i, (mid, task, dl, lk) in enumerate(models, 1):
-        dl_fmt = f"{dl:,}" if dl else "N/A"
-        lk_fmt = f"{lk:,}" if lk else "N/A"
-        rows += f"| {i} | [{mid}](https://huggingface.co/{mid}) | `{task}` | {dl_fmt} | {lk_fmt} |" + NL
-    return header + rows
-
-def recommend_model(
-    modality, outputs, domains, regulated,
-    data_sensitivity, volume, latency,
-    context_size, customization
-):
-    # Collect HF tasks to query
-    hf_tasks = []
-    for m in modality:
-        hf_tasks.extend(TASK_MAP.get(m, []))
-    # Deduplicate, keep order
-    seen_t = set()
-    unique_tasks = []
-    for t in hf_tasks:
-        if t not in seen_t:
-            seen_t.add(t)
-            unique_tasks.append(t)
-    if not unique_tasks:
-        unique_tasks = ["text-generation"]
-
-    license_filter = LICENSE_FILTER.get(data_sensitivity)
-
-    # Fetch live models
-    live_models = fetch_top_models(unique_tasks, license_filter=license_filter, top_n=5)
-    model_table = format_model_table(live_models)
-
-    # Deployment path
-    if data_sensitivity == "Strict: prefer on-prem / VPC only":
-        deploy_path = "🚀 **Private Self-Hosted** - Run top open-source models above via Ollama or vLLM on your own infra."
-    elif volume == "100,000+":
-        deploy_path = "💰 **Cost-Optimized Scale** - Use provisioned throughput for closed models, or self-host on GPU clusters."
-    else:
-        deploy_path = "⚡ **Serverless API** - Closed models: OpenAI/Anthropic/Google APIs. Open-source: HF Inference Endpoints."
-
-    # Smart tips
-    tips = []
-    if any(d in ["Healthcare", "Finance", "Legal"] for d in domains):
-        tips.append("🔒 **Zero Data Retention (ZDR)** - For regulated domains, enable ZDR on OpenAI/Anthropic/Google or use on-prem.")
-    if latency == "< 500 ms (Instant)":
-        tips.append("⚡ **Pick a smaller distilled variant** - Sort by 'likes' and look for 7B/8B versions of the top model.")
-    if context_size == "32K - 200K tokens (Long)":
-        tips.append("📚 **Add a RAG layer** - Even for long-context models, pair with Pinecone/Weaviate/pgvector for accuracy.")
-    if "Style fine-tuning" in customization:
-        tips.append("🎨 **Fine-tune with QLoRA** - Take the top model from the table above and fine-tune on 1k-5k domain examples.")
-    if "RAG + Tool Calling" in customization:
-        tips.append("🔧 **Enable Tool Calling** - Most top models support function calling / tool use. Check the model card for schema.")
-    if not tips:
-        tips.append("✨ Start with the #1 model in the table above and iterate. A great system prompt gets you 80% of the way.")
-
-    tips_text = (NL + "- ").join(tips)
-
-    how_to = ("### 📋 Your 3-Step Rollout Guide" + NL
-        + "1. **Sandbox (Week 1):** Clone the top 3 models from the table above. Run 50-100 real queries from your dataset." + NL
-        + "2. **Evaluate (Week 2):** Score on accuracy, latency, and cost per 1K tokens. Eliminate bottom performers." + NL
-        + "3. **Deploy (Week 3):** Integrate the winner via API or self-hosted endpoint. Set up monitoring with LangSmith or Helicone." + NL
-    )
-
-    cta = (
-        "---" + NL
-        + "💡 **Need help shipping this fast?** [Book a free 30-min strategy call with AnkTechsol](https://anktechsol.com) "
-        + "- we'll map your AI stack, estimate costs, and give you a production roadmap. No fluff."
-    )
-
-    tasks_label = ", ".join(f"`{t}`" for t in unique_tasks)
-    license_label = f"license: `{license_filter}`" if license_filter else "all licenses"
-
-    summary = (
-        "## 🎉 Your Live AI Model Strategy" + NL + NL
-        + "### 📦 Top Models on HuggingFace Hub Right Now" + NL
-        + f"*Live from HF Hub API | Tasks: {tasks_label} | Filter: {license_label} | Sorted by downloads*" + NL + NL
-        + model_table + NL + NL
-        + "### 🗺️ Best Deployment Path" + NL
-        + deploy_path + NL + NL
-        + "### 💡 Pro-Tips for Your Use Case" + NL
-        + "- " + tips_text + NL + NL
-        + how_to + NL
-        + "---" + NL
-        + cta
-    )
-    return summary
-
-with gr.Blocks(theme=gr.themes.Soft(primary_hue="orange", secondary_hue="slate"), title="AI Model Picker | AnkTechsol") as demo:
-    gr.Markdown("# 🚀 AI Model Selection Wizard")
-    gr.Markdown("**Pick the perfect AI brain for your use case.** This tool queries the HuggingFace Hub live and returns the top trending models for your exact task - no hardcoded lists. By [AnkTechsol](https://anktechsol.com).")
-    with gr.Row():
-        with gr.Column(scale=1):
-            gr.Markdown("### 🔍 Step 1: Describe Your Task")
-            modality = gr.CheckboxGroup(label="What inputs are you working with?", choices=["Text in -> text out", "Images in (docs, UI, scans)", "Audio in (speech)", "Structured data (tables, logs)", "Code"])
-            outputs = gr.CheckboxGroup(label="What output do you need?", choices=["Natural language answer / summary", "Classification / tagging", "Field extraction from text/PDF", "Content generation (copy, emails)", "Scoring / ranking / decision"])
-            domains = gr.CheckboxGroup(label="Your Industry / Domain", choices=["General / consumer", "Ecommerce / SaaS", "Finance", "Healthcare", "Legal", "Internal enterprise knowledge"])
-            regulated = gr.CheckboxGroup(label="Data Sensitivity", choices=["Yes - contains PII, PHI, or financial data", "No - mostly public or non-sensitive data"])
-        with gr.Column(scale=1):
-            gr.Markdown("### ⚙️ Step 2: Set Your Constraints")
-            data_sensitivity = gr.Radio(label="Data Privacy Requirements", choices=["Standard SaaS API is fine", "Must stay in a specific cloud region", "Strict: prefer on-prem / VPC only"], value="Standard SaaS API is fine")
-            volume = gr.Radio(label="Expected Daily Request Volume", choices=["< 1,000", "1,000 - 100,000", "100,000+"], value="< 1,000")
-            latency = gr.Radio(label="Latency Requirement", choices=["< 500 ms (Instant)", "0.5 - 5 s (Standard)", "> 5 s (Batch)"], value="0.5 - 5 s (Standard)")
-            context_size = gr.Radio(label="Max Context Window Needed", choices=["< 4K tokens (Short)", "4K - 32K tokens (Medium)", "32K - 200K tokens (Long)"], value="4K - 32K tokens (Medium)")
-            customization = gr.CheckboxGroup(label="Customization Needs", choices=["Prompt engineering only", "Style fine-tuning", "RAG + Tool Calling"])
-    submit_btn = gr.Button("✨ Fetch Live Models and Generate My Strategy", variant="primary", size="lg")
-    gr.Markdown("> 🕐 *Querying HuggingFace Hub live - may take 5-10 seconds. Please wait after clicking.*")
-    gr.Markdown("---")
-    output = gr.Markdown(label="Your Live AI Strategy")
-    submit_btn.click(fn=recommend_model, inputs=[modality, outputs, domains, regulated, data_sensitivity, volume, latency, context_size, customization], outputs=output)
-    gr.Markdown("---")
-    gr.Markdown("### 🛠️ Why Teams Trust AnkTechsol")
-    gr.Markdown("We help startups and enterprises go from AI confusion to production-grade AI systems. Fine-tuning, RAG pipelines, GPU inferencing as a service - we deliver.")
-    gr.Markdown("**[Visit anktechsol.com](https://anktechsol.com) | [LinkedIn](https://www.linkedin.com/company/anktechsol)**")
-
-if __name__ == "__main__":
-    demo.launch()
+Final fix: Lead form + Email CTA colab@anktechsol.com